Senior Manager, Data Science - Marketing Analytics & Data Science

We are looking for a rigorous, systems-minded quantitative leader to own our core commercial intelligence engine end-to-end. This is not a traditional dashboard-building or BI reporting seat. You will define, build, and encode the mathematical logic, unit-economic models, and predictive forecasting systems that power our product and drive real commercial decisions for our brands.

You will sit at the intersection of finance, data science, and product engineering, turning complex commercial data, messy founder inputs, and predictive models into defensible, automated product logic and AI agent workflows.

Key Accountabilities

1. Proprietary Systems: Growth Forecasts & P&Ls

• Growth Forecasting: Build and own the methodology that takes a brand’s current performance and projects a defensible 12-month forecast, complete with a rigorous assumption set.
• Financial Architecture: Construct brand-level P&Ls directly from the forecast, mapping revenue, COGS, contribution margin, and operating costs.
• Variance & Calibration: Continuously run models against real outcomes, measure errors, track actuals against projections, and explain every variance.

2. Productization & Agentic Workflow Design

• Product Logic: Translate sophisticated financial and statistical models into production-ready product logic so the system calculates and delivers outputs natively.
• AI Agent Specification: Define what data agents compute, what internal data/benchmarks they draw on, and what assertions they are strictly barred from making.
• Human-in-the-Loop Orchestration: Replace manual validation with automated product rules while establishing clear review gates for human sign-off before deliverables ship.
• Data Schemas & Engineering Collaboration: Specify data structures, validation rules, and pipelines across structured commercial inputs, partnering directly with engineering to bring models to life in production.

3. Decision Framing & Commercial Rigor

• Decision-First Modeling: Design every analysis backwards from the specific commercial decision it is meant to change.
• Uncertainty & Stated Confidence: Size uncertainty statistically, state clearly what sparse or self-reported data cannot support, and attach a stated confidence level to every output.
• Benchmark Library Management: Define and version the benchmark repository (category, stage, market) to evaluate brands accurately even when data is sparse.

4. Enablement & Commercial Communication

• Cross-Functional Communication: Present complex numbers, models, and uncertainties to founders and operators with clarity and authority.
• Quantitative Literacy: Raise the analytical bar across the business by teaching teams how to read and interpret model outputs correctly.
• Documentation & Standards: Write airtight documentation and standards for data/finance deliverables so cross-functional delivery teams can execute autonomously.

What This Seat Is Not

• Not a BI/Reporting Seat: Dashboards are merely one downstream output of this work; your primary focus is proprietary modeling and product architecture.
• Not Research Without Action: We judge models by the decisions they actually changed, not academic complexity.
• Not a Data Engineering Seat: You will specify pipelines, schemas, and logic for engineering to build—you won't be maintaining core infrastructure tables yourself.

Ideal Background & Qualifications

Must-Haves

• Experience: 6–8 years in growth, marketing, commercial, or product analytics within DTC, e-commerce, marketplaces, or specialized platforms.
• Proven Track Record: End-to-end ownership of a forecasting, unit-economics, or media-mix model that commercial teams relied on for high-stakes budget or strategic decisions. You can name the decisions your model changed and what followed.
• DTC Unit Economics Mastery: Deep fluency in CAC, AOV, COGS, contribution margin, LTV, payback period, breakeven, retention cohorts, and working capital reconciliation.
• Modelling with Incomplete Data: Demonstrated ability to build robust outputs from banded, self-reported, or partial founder inputs while explicitly quantifying confidence levels.
• Statistical Rigor: Strong foundation in sizing uncertainty, managing sample limitations, and maintaining analytical integrity.
• Technical Depth: Hands-on daily fluency in SQL and Python.
• Product & Engineering Collaboration: Experience working directly with engineering teams to turn quantitative models or metric definitions into production software.
• Communication: Exceptional presence and ability to defend underlying assumptions and model uncertainty under direct challenge.

Nice-to-Haves

• Media mix modeling (MMM), incrementality testing, or causal measurement methodologies.
• Applied machine learning experience (forecasting, propensity, or segmentation models in production).
• Experience evaluating generative AI or multi-agent system outputs.
• Familiarity with modern data stack layers (Snowflake, BigQuery, dbt) and attribution platforms (Triple Whale, Northbeam, Rockerbox).
• Exposure to multi-brand ecosystems via agency, consulting, or platform environments.
• Experience in early-stage product environments where models and products were co-developed from scratch.

We are looking for a hands-on Lead Data Scientist with strong analytical, machine learning, and problem-solving skills to work in a client-facing environment.

The role requires someone who can independently identify opportunities, formulate hypotheses, design solutions, and drive initiatives from analysis through experimentation and production implementation. The candidate should also be comfortable guiding team members and working closely with engineering and client stakeholders.

Responsibilities: 

• Analyse complex datasets to identify patterns, issues, and opportunities.
• Formulate and validate hypotheses through structured analysis and experimentation.
• Build and improve machine learning and anomaly detection solutions.
• Design end-to-end solutions considering data, modelling, engineering, and production constraints.
• Perform root cause analysis across models, data, and systems.
• Work with engineering teams on feature pipelines, model inference, and production deployment.
• Lead technical discussions with clients and communicate recommendations, trade-offs, and expected impact.
• Guide team members on analysis, modelling, and solution design.
• Proactively identify initiatives and roadmap items that can add value to the project.

Requirements: 

• Strong foundation in statistics and machine learning.
• Strong hands-on experience with Python and SQL.
• Hands-on experience with Python ML and data frameworks such as Pandas, NumPy, scikit-learn, TensorFlow/Keras, Dask, Matplotlib, Boto3 SageMaker Python SDK, and Horovod.
• Strong data analysis, hypothesis-generation, and problem-solving skills.
• Experience with standard supervised and unsupervised ML techniques.
• Experience with anomaly detection techniques such as Isolation Forest and Autoencoders.
• Experience building and deploying production ML solutions.
• Working knowledge of data engineering and real-time / batch inference environments.
• Strong communication and stakeholder-management skills.
• Ability to lead technical work and guide cross-functional teams.

Good to Have: 

• Experience in fraud detection or risk modelling.
• Experience with Graph Neural Networks.
• Exposure to real-time systems, streaming features, and low-latency data stores.
• Experience in AdTech, e-commerce, gaming, mobile applications, or similar high-volume consumer platforms.

We are looking for a Data Science & Machine Learning Senior Associate with 3–5 years of relevant experience in data science, machine learning, and analytics. The candidate will be responsible for developing predictive models, analyzing complex datasets, building scalable ML solutions, and supporting production-grade data science applications on cloud platforms.

• Develop and implement machine learning and predictive analytics models.
• Perform data analysis, statistical modeling, and optimization to solve business problems.
• Build demand forecasting and predictive models using time-series and other advanced techniques.
• Work with large datasets using Python, SQL, PySpark, and cloud-based data platforms.
• Develop and maintain scalable data pipelines for ML model development and deployment.
• Implement MLOps practices including model deployment, monitoring, retraining, and data-drift detection.
• Collaborate with software engineers, product teams, and business stakeholders to convert business requirements into analytical solutions.
• Validate models for accuracy, robustness, bias, and production readiness.
• Create meaningful visualizations and communicate analytical insights to technical and non-technical stakeholders.

• Python
• SQL
• Data Science & Machine Learning
• Predictive Modeling
• Statistical Analysis
• Machine Learning Algorithms
• Optimization Techniques
• Google Cloud Platform (GCP)
• BigQuery
• Dataflow
• Dataproc
• Data Fusion
• Cloud SQL
• Airflow
• PySpark
• PostgreSQL
• Terraform
• Tekton
• APIs
• MLOps

• Java

• Demand Forecasting
• Time-Series Analysis
• Neural Networks
• Ensemble Methods
• Support Vector Machines (SVM)
• Regression and Cluster Analysis
• ML Model Testing
• Bias Detection and Data Drift Monitoring
• Production ML Deployment
• QlikSense
• Automotive or Supply Chain Analytics

Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, Mathematics, or a related technical field.Master's degree in a relevant quantitative or technical field is preferred.

🚀 We’re Hiring | Data Scientist 🧠📊

Ready to turn data into real-world intelligence? Join us and work on exciting AI/ML & data-driven solutions!

🔹 Experience: 8+ Years

🔹 Must-Have Skills:

🐍 Python | 🤖 Machine Learning | ☁️ Cloud | 🧠 NLP | 📊 Data Visualization

📍 Location: Pune

💼 Work Mode: Work from Office

If you're passionate about Data Science, AI & solving complex business problems, we’d love to hear from you!

📩 Interested? Kindly text

#Hiring #DataScientist #DataScience #MachineLearning #Python #NLP #AI #Cloud #DataVisualization #TechJobs #HiringNow

Position Overview:

We are building a new retail merchandising platform on top of that data. It answers what to stock, how much,

and in which store—combining sales, catalogue, and customer data with GeoIQ’s location attributes at retail

scale: thousands of stores, tens of thousands of SKUs, decisions made weekly.

As part of the data science team on this platform, you will build the forecasting, assortment and

recommendation models behind those decisions. The platform is new, so the models are being built now, not

maintained. Hands-on modelling with senior review — what you ship changes what a store actually orders.

Responsibilities:

● Build SKU- and store-level demand forecasts, including for new products with little or no sales history.

● Build assortment models — which products earn shelf space in which stores or store clusters, and how

a range change is likely to play out.

● Build recommendation and ranking models for product discovery, cross-sell and bundling, including

cold-start for new products and customers.

● Engineer features from sales, seasonality, promotions, pricing, catalogue hierarchy, store attributes and

GeoIQ’s location and demographic layers.

● Handle the realities of retail data: intermittent demand, stockouts censoring true demand, returns and

cannibalisation between similar SKUs.

● Design backtests and A/B tests that reflect how planners use the output; monitor deployed models for

drift.

● Build data pipelines and work with engineering to take models to production.

● Communicate findings to merchandising, category, and product teams.

Expectations:

● Proficient in Python

● Work experience: 1–3 years

● Prior experience with building ML models end-to-end

● Prior experience with SQL

● Knowledge of statistical techniques and model evaluation

● Experience with time-series forecasting or recommender systems will be an added advantage

● Experience with retail, e-commerce, supply chain, or spatial data will be an added advantage

Join us early and help build the merchandising brain for retail at national scale — close to the product and the

customer, with the freedom of a platform still being shaped.

About Starbond

Starbond is a growing direct-to-consumer brand focused on delivering high-quality products and an excellent customer experience. We are looking for a hands-on Marketing Manager who can turn data, customer insights, and strong execution into profitable growth.

About the Role

The Marketing Manager will own Starbond’s direct-to-consumer growth engine across paid acquisition, retention, lifecycle marketing, content, SEO, answer engine optimization (AEO), social media, conversion optimization, and agency performance. This role requires a strong command of unit economics—including customer lifetime value (LTV), customer acquisition cost (CAC), CAC payback period, gross margin, and contribution margin—and the ability to convert these metrics into sound growth decisions.

You will directly manage marketing agencies across Meta, Google, email, and AEO while leading the internal Marketing team, setting clear performance standards, and giving the CEO timely visibility into results, risks, and recommended actions.

Key Responsibilities

DTC Growth and Customer Acquisition

• Develop and lead the DTC acquisition strategy across Meta Ads, Google Ads, Shopify, email, SEO, social media, Amazon alignment, and other relevant channels.
• Manage marketing for profitable growth—not revenue or ROAS alone—by incorporating product margin, contribution margin, retention, and customer quality into decisions.
• Establish acceptable CAC thresholds by product, customer segment, campaign, and channel, and recommend when to scale, restructure, reduce, or discontinue investment.
• Align advertising, creative, landing pages, offers, product positioning, inventory availability, and operational readiness.

LTV, Retention, and Lifecycle Marketing

• Maintain a clear and consistent LTV methodology using customer revenue, purchase frequency, retention, gross margin or contribution margin, and cohort data.
• Analyze LTV and retention by acquisition source, product, cohort, and customer segment, and use the findings to improve channel and offer decisions.
• Lead strategies that improve repeat purchase rate, purchase frequency, average order value, cross-sell, replenishment, retention, and win-back performance.
• Oversee email and lifecycle marketing quality, segmentation, deliverability, revenue contribution, and customer communication accuracy.

Paid Advertising and Conversion

• Oversee paid media strategy, budget pacing, campaign structure, audience strategy, creative testing, attribution, and landing-page performance.
• Establish clear testing, scaling, optimization, and stop-loss rules, and require decisions to be supported by sufficient data and financial context.
• Review performance at the channel, campaign, audience, creative, product, and cohort levels.
• Partner with the Web Developer on Shopify priorities, site performance, conversion rate optimization, tracking, and quality assurance.

Agency and Vendor Management

• Directly manage marketing agencies across Meta, Google, email, and AEO, including scopes of work, deliverables, timelines, communication standards, SLAs, KPIs, reporting, ownership, and escalation procedures.
• Use agency scorecards and recurring performance reviews to track KPI attainment, SLA compliance, output quality, timeliness, efficiency, and business results.
• Require root-cause analysis and time-bound corrective action plans for missed targets, SLA breaches, quality issues, reporting gaps, or inefficient spend.
• Independently validate agency recommendations and recommend contract expansion, renegotiation, reduction, replacement, or termination when appropriate.
• Ensure Starbond retains access to and ownership of advertising accounts, data, dashboards, creative files, documentation, and intellectual property.

Content, Creative, and Brand

• Maintain an active content and creative pipeline supported by clear briefs, owners, deadlines, QA standards, and success metrics.
• Ensure reliable content-production support is available when external resources are required.
• Create a structured process for testing creative concepts, hooks, formats, messages, offers, and calls to action.
• Protect product positioning and perceived value by avoiding excessive reliance on discounting and ensuring marketing claims are accurate and supportable.
• Ensure approved assets are properly named, organized, and stored in company-owned systems.

Team Leadership, Process, and Reporting

• Lead the internal Marketing team by setting clear responsibilities, deadlines, quality standards, KPIs, communication expectations, and individual performance targets.
• Conduct regular coaching, one-on-ones, performance reviews, and accountability conversations; identify skill gaps and coordinate development or corrective action with HR.
• Make timely recommendations regarding training, role clarity, workload changes, reassignment, or other performance actions when standards are not met.
• Create and maintain marketing processes, approval flows, reporting definitions, campaign documentation, and quality-control systems.
• Maintain a weekly marketing scorecard that distinguishes leading and lagging indicators and explains what changed, why it changed, and what action is required.
• Proactively escalate material risks and present concise, financially grounded recommendations to the CEO.

What Success Looks Like

• Marketing data, forecasts, campaign reports, creative briefs, and recommendations are accurate, validated, and financially grounded.
• Campaigns, agency deliverables, reports, corrective actions, and launches are completed on time, with blockers escalated early.CAC, LTV, LTV:CAC, MER, ROAS, contribution margin, retention, and cohort performance are consistently defined and used to guide decisions.
• Marketing investment produces profitable customer growth, stronger retention, faster learning, higher-quality execution, and stronger brand protection.
• The Marketing team and external agencies operate with clear ownership, measurable standards, and consistent accountability.

What We’re Looking For

• Proven experience leading DTC or e-commerce marketing across paid acquisition, retention, lifecycle marketing, content, and conversion optimization.
• Strong working knowledge of CAC, LTV, LTV:CAC, MER, ROAS, contribution margin, retention, cohort analysis, and marketing attribution.
• Experience managing Meta Ads, Google Ads, email marketing, SEO, AEO, Shopify, and agency partners.
• Demonstrated ability to manage budgets and make channel decisions based on profitability and customer quality.
• Experience leading and developing marketing team members, setting KPIs, and managing performance.
• Strong analytical judgment with the ability to challenge assumptions, validate agency recommendations, and translate data into clear actions.
• Excellent written and verbal communication, with a proactive and concise reporting style.
• Highly organized, accountable, and comfortable working in a remote, fast-moving environment.

Role Overview 

As a Data Scientist, you will work with business stakeholders, AI engineers, and domain experts to transform data into actionable insights and intelligent solutions. You will develop machine learning models, perform statistical analysis, and contribute to AI-driven products that create measurable business impact. 

Key Responsibilities 

Data Science & Machine Learning 

• Analyze structured and unstructured data to identify patterns, trends, and business opportunities.  
• Perform exploratory data analysis (EDA), feature engineering, and data preparation.  
• Develop, evaluate, and optimize machine learning models for prediction, classification, clustering, and forecasting.  
• Apply statistical techniques to solve business problems and validate model performance.  
• Design and execute experiments to improve model accuracy and business outcomes.  

 AI Solution Development 

• Collaborate with AI Engineers, Data Engineers, and domain experts to build AI-powered solutions.  
• Translate business requirements into scalable data science approaches.  
• Contribute to Generative AI and advanced analytics initiatives where applicable.  
• Document methodologies, model performance, and key findings.  

 Required Technical Skills 

• Strong programming skills in Python and SQL for data analysis, feature engineering, and machine learning.  
• Strong understanding of Statistics, Probability, Linear Algebra, and Calculus as applied to machine learning and data science.  
• Experience with Exploratory Data Analysis (EDA), data preprocessing, feature engineering, feature selection, and handling missing or imbalanced data.  
• Good understanding of Supervised, Unsupervised, and Ensemble Machine Learning algorithms, including their assumptions, strengths, limitations, and appropriate use cases.  
• Strong knowledge of Regression, Classification, Clustering, Time Series Forecasting, Dimensionality Reduction, Recommendation Systems, and Anomaly Detection techniques.  
• Experience with Model Evaluation, Cross-Validation, Hyperparameter Optimization, Bias-Variance Trade-off, Feature Importance, Explainable AI (XAI), and Performance Metrics.  
• Understanding of Statistical Inference, Hypothesis Testing, Probability Distributions, Sampling Techniques, Confidence Intervals, and A/B Testing.  
• Experience translating business problems into analytical approaches and developing scalable, data-driven solutions.  
• Working knowledge of Generative AI, Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG) is preferred. 

Preferred Qualifications 

• Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, Statistics, Mathematics, Engineering, or a related field.  
• 2–4 years of experience developing machine learning or data science solutions.  
• Experience working on end-to-end data science projects in a business environment.  

 Nice to Have 

• Exposure to Generative AI, LLMs, RAG, or Agentic AI.  
• Experience with Computer Vision or Natural Language Processing (NLP).  
• Familiarity with cloud-based AI platforms.  
• Knowledge of construction, engineering, manufacturing, or industrial domains.  
• Participation in hackathons, research, Kaggle competitions, or open-source projects.  

 Soft Skills 

Strong analytical and problem-solving skills, effective communication and collaboration, ownership mindset, adaptability, continuous learning, and a passion for innovation.

CraftMyPlate is a Hyderabad-based platform for micro-events: house parties, birthdays, office celebrations, and small gatherings. One thing hasn't changed since day one. Every order comes from someone making a specific, personal decision: this occasion, this many people, this budget, this date. We win by understanding that decision better than anyone else does.

We are now investing in the systems (data, growth, and eventually AI-driven automation) that let us make that same quality of decision at scale, without needing to grow headcount just to keep up. That investment is partly backed by funding we recently brought in, but the funding is not the story. It simply means we can now hire someone senior enough to own this problem properly, instead of solving it in pieces.

If you join now, you are not joining a company that has already figured everything out and just needs someone to run the machine. You are joining at the point where the machine is still being designed. What you build in the next year will still be the foundation two years from now.

About the Role

We are hiring a Growth Lead to own two things at once: how many people know about us, and how much revenue that awareness turns into. Not one or the other. Both, as a single connected number.

Here is what makes this role different from most growth roles you have seen: the infrastructure is already built. Tracking, attribution, and engagement systems are already in place and working. Our content and performance teams already execute well within the current playbook. You are not being hired to set any of that up. You are being hired to direct it and extract return from it.

That means your job is judgment, not motion. You decide which channels get more budget and which get cut. You decide what message goes to which segment. You decide what "working" means, and you prove it with numbers, not activity. The team executes. You are accountable for the return.

We need someone who has done this before, at a company where marketing spend had to answer to revenue, not just to reach. Someone who can point to a previous role and say plainly: this is what we spent, this is what we made back, and this is what I specifically changed to move that number.

What "Good" Looks Like (Market Standard)

This is not a vague "be strategic" ask. In most growth-stage consumer companies, a Growth Lead's performance is judged against a small set of hard numbers. We expect the same here:

• Return on ad spend (ROAS) or marketing efficiency ratio (MER) improving quarter over quarter across paid channels, not just held flat
• CAC to LTV ratio healthy enough to support scaling spend, not just acquiring volume (a commonly cited industry bar is a ratio meaningfully above 1:3, CAC to LTV)
• CAC payback period short enough that growth doesn't starve cash flow
• Blended revenue attributable to marketing-led channels, tracked and defended, not assumed
• A steady pace of new channel or approach tests per quarter, with a clear kill or scale decision made on each one, not experiments left to run forever without a verdict

If these terms are unfamiliar to you or you have never been held to them, this is probably not the right role yet. If you already think in these terms by habit, this role gives you the platform to prove it at a larger scale.

What You'll Own

• Awareness and reach across channels: Grow how many of the right people know about us, across paid, organic, and content channels, without confusing reach for revenue
• Revenue and ROI: Own the return on every rupee spent across channels. You inherit working tracking systems (CleverTap, AppsFlyer, Mixpanel); your job is to use them to make better spend decisions, not to build or maintain them
• Segmentation strategy: Build and keep refining how we segment customers by occasion type, guest count, budget band, and lead time to event. Turn that into distinct messaging and targeting for each segment, not one-size-fits-all campaigns
• Growth and channel scaling: Identify, test, and scale marketing channels and approaches beyond what we currently run, with a clear view of what each one returns
• Team direction: Directly lead our content and performance marketing team. You set the direction and the bar. They execute against it. You are not doing their job for them, and you are not letting them run without direction either
• Reporting: Give the founders and CMO clear, ROI-level visibility. What is working, what isn't, what you are cutting, and what you are doubling down on, and why

Required Skills & Background

• A proven, quantifiable track record of improving marketing ROI at a previous company, across more than one channel, not just optimizing a single ad account. Able to state clearly what the return was before, what it became, and what they specifically did to move it.
• 5 to 8 years in marketing, with real time spent owning revenue and channel-level ROI, not just campaign execution
• Experience running and defending a multi-channel budget: knowing when to shift spend between awareness and performance channels based on what the numbers say
• Strong quantitative fluency. Comfortable reading cohort, retention, and attribution data (from tools like CleverTap, AppsFlyer, and Mixpanel) and turning it into a spend or strategy decision, without needing to build the pipelines themselves
• Demonstrated experience building segmentation-led marketing strategy. Able to show, concretely, how they tailored messaging to distinct customer segments in a past role
• Experience managing a team (content and/or performance). This role leads people, not just plans
• Tier-1 educational background (top B-school, or a top engineering institute with demonstrated growth ownership). Exceptions made for candidates with a clearly exceptional track record
• Consumer internet, D2C, e-commerce, or marketplace background strongly preferred. Extra credit for a business with real customer-segment diversity, not a single-SKU product

Good to Have

• Experience marketing a business where the customer's decision changes a lot by occasion, timing, or budget (events, travel, gifting, weddings)
• Experience balancing brand/awareness spend against direct-response spend, and defending that split with numbers
• Prior experience at an early-stage startup where they built the growth function rather than inheriting one
• Hands-on comfort reviewing ad creative and content briefs, even if they are not writing them personally

What We Offer

• Direct ownership of the marketing function, reporting to the founders and CMO
• Working infrastructure from day one: tracking, attribution, and engagement systems are already built, so you spend your time on strategy and ROI, not setup
• Competitive pay, with ESOP and ownership options so your upside grows with the company's
• A team that already executes well. Your job is to point it in the right direction, not build it from zero
• Real accountability: revenue and ROI, not vanity metrics
• Fast growth and more scope as the company scales

Help new products find their first customers in the US, and teach our systems how you do it.

Daemon Ventures is a US technology company that builds and runs internet businesses with a very small team and heavy use of AI. We are looking for a growth lead who has personally taken products from zero to their first paying customers in the US market.

What you'll do

- Review how each new business goes to market: the plan, the actual ads, landing pages, emails and sign-up flow, and the numbers that come back. Say clearly what to change, and why.

- Diagnose. When a number is bad, work out whether the problem is the audience, the offer, the page or the channel, and what to try next.

- Turn what works into repeatable playbooks, so every future launch starts from what the last one learned.

- Bring in specialists (search, outbound, paid social, communities, marketplaces) when a problem needs deep craft, and judge their work.

Who you are

- Senior and hands-on. 6–10 years in growth or marketing, at least 3 of them personally acquiring US customers for a software product, an app or an online business. You ran the channels yourself, not only through agencies or a team.

- Taken a product from near zero to real US traction, with numbers you can show.

- A diagnostician. You can read a funnel and find the leak, and you think in experiments. You read results honestly, including when the answer is "this channel doesn't work for this product."

- A clear writer. You explain your reasoning so that others can act on it.

- Energized by working alongside AI. Most execution here is automated; your judgment is what makes it better.

Details

- Full-time and remote within India, engaged as an independent contractor.

- ₹45–55 LPA based on experience, plus equity.

- Process:

1. A short application with a 20-minute exercise.
2. A conversation with the founder.
3. A second conversation.

To apply: https://docs.google.com/forms/d/e/1FAIpQLSdB-KJIUnkbNbXWCdqJmTEFGbHglT5_2wqIcsVPN_QKaQTG1A/viewform

The application includes the exercise. Please don't send a CV alone.

About the Role

Antier Solutions is looking for a mature, highly capable, and comprehensive Marketing Manager to lead our global marketing department.

In this role, you will act as the anchor and orchestrator for our entire marketing ecosystem. You will be directly responsible for managing, mentoring, and scaling a diverse in-house team of Content Writers, Social Media Marketers (SMM), Performance Marketers (PPC), Community Managers, and SEO Specialists.

We need a leader who can take broad business goals from the leadership team and translate them into actionable, high-yielding marketing campaigns. The ideal candidate must possess the unique ability to manage both creative minds (Writers/Designers) and data-driven analytical profiles (PPC/SEO), aligning them to drive predictable inbound leads.

Prior experience in high-performance IT services or B2B tech companies is highly preferred.

Key Responsibilities

1. Team Leadership, Workflows & Resource Management

Cross-Functional Alignment: Oversee the daily operations of all marketing sub-departments (Content, SMM, PPC, SEO, and Community). Break down departmental silos to ensure smooth, agile collaboration.

Process Standardization: Design and enforce structured workflows using internal project management tools. Establish clear KPIs, content delivery calendars, and campaign turnaround timelines (SLAs).

Resource Optimization: Manage internal team bandwidth effectively. Balance the workload of writers and designers to ensure high-quality output without execution bottlenecks.

2. End-to-End Inbound Strategy & Lead Generation

Campaign Architecture: Design, execute, and monitor comprehensive multi-channel inbound marketing strategies tailored for global enterprise clients (B2B).

Funnel Optimization: Take ownership of the marketing automation tech stack to build lead-scoring models, behavioral email nurturing, and landing page optimization.

Paid & Organic Balance: Supervise paid acquisition strategies (Google Ads, LinkedIn Ads, Meta Ads) managed by the PPC team, ensuring they complement the long-term organic growth driven by the SEO and Content teams.

3. Core Marketing Operations (Budgeting & Analysis)

Budget ROI Allocation: Plan, allocate, and manage the monthly and quarterly marketing budget across paid ads, software tools, and external resources, ensuring minimum leakage and maximum ROI.

Competitor & Market Intelligence: Conduct ongoing competitor mapping and trend analysis in the tech service sector to keep Antier's positioning ahead of the market.

Vendor & Agency Management: Evaluate and manage relationships with external vendors, freelance creators, or PR agencies when scaling requires outsourcing.

4. Brand Governance & Community Advocacy

Unified Brand Voice: Act as the ultimate gatekeeper for Antier's global brand identity. Ensure that everything-from a 2-line tweet by SMM to a 2000-word whitepaper by the content team-maintains absolute professional consistency.

Community Engagement: Guide the Community Managers to design retention and engagement strategies that turn users/followers into active brand advocates.

5. Analytics & Leadership Reporting

Data Consolidation: Build and maintain unified tracking dashboards (using GA4, CRM data, and platform metrics) to get a clear picture of marketing performance.

Executive Reviews: Present deeply analytical weekly and monthly reports to the Management, focusing on metrics like Customer Acquisition Cost (CAC), pipeline velocity, and conversion rates.

Tech Innovation & AI Automation

While this is a core team-leadership and strategic role, the candidate must have a sharp eye for modern tech depth. The ideal candidate should have hands-on experience or deep workflow knowledge of AI automation via AI Agents to eliminate and automate repetitive tasks across marketing functions. This includes leveraging technology to optimize the writing team's research speed, scaling creative asset variations for SMM/PPC, running automated report compilation, and setting up automated conversational bots to triage inbound traffic within our community channels effectively.

Required Profile & Core Competencies

Experience Matrix: 8-12 years in B2B/IT Services or SaaS marketing, showing a clear career progression from an execution specialist to a Manager.

Domain Fluency: Must be able to speak the language of all sub-departments fluently. You should be able to critique ad copies with writers, discuss CPA/Quality Scores with PPC, analyze Core Web Vitals with SEO, and review engagement strategies with Community Managers.

Problem-Solving & Crisis Management: Ability to handle sudden dips in traffic/leads or manage PR/social media crises with calm, structured communication.

Tools Knowledge: Advanced proficiency with CRM & Automation hubs (HubSpot/Marketo), Analytics (GA4/Google Search Console), and collaboration tools.

Soft Skills: High emotional intelligence (EQ), leadership capability to manage diverse creative/technical teams, and exceptional written and verbal English communication.