Lead Data Analytics

Job Title: Senior Data Analyst

Location: Sharnam Metrolinks, Amraiwadi, Ahmedabad

Working Days: 6 Days a Week

Department: MDO

Reporting To: COO

Employment Type: Full-Time

Job Summary

We are seeking a highly analytical and detail-oriented Senior Data Analyst to lead data management, reporting, automation, and system development initiatives across the organization.

The role focuses on transforming raw data into meaningful insights, designing and implementing automated systems, strengthening data structures, and supporting cross-functional teams with accurate, actionable intelligence to drive strategic and operational business decisions.

Key Responsibilities

1. Data Management & Reporting

● Collect, clean, validate, and consolidate data from multiple internal and external sources.

● Prepare and deliver daily, weekly, and monthly MIS reports for management and departments.

● Design, develop, and maintain dashboards to track KPIs, performance metrics, and operational trends.

2. Database & Data Accuracy Management

● Manage and regularly update internal databases, spreadsheets, and reporting systems.

● Ensure data accuracy, consistency, integrity, and confidentiality across all platforms.

● Implement best practices for data validation, version control, and audit checks.

3. Data Analysis & Business Insights

● Analyze large and complex datasets to identify trends, patterns, gaps, and anomalies.

● Translate data findings into clear, actionable insights and recommendations to support

strategic and operational decision-making.

4. Reporting Automation, System Recommendation & Implementation

● Identify opportunities to replace manual or semi-manual processes with automated, data-driven systems.

● Design and implement automated reporting frameworks, dashboards, and data pipelines using Excel (Power Query, VBA, Macros), SQL, BI tools, and Python.

● Proactively suggest new automation tools, system enhancements, or integrations to

improve efficiency, accuracy, and scalability.

● Lead the end-to-end implementation of approved automation initiatives, including requirement gathering, system design, testing, deployment, and stabilization.

● Continuously monitor and optimize automated systems in line with business growth and evolving data needs.

5. Cross-Functional Coordination & Support

● Collaborate with Sales, HR, Finance, Operations, and other departments to understand

reporting and data requirements.

● Provide support for ad-hoc analysis, custom reports, and special data requests.

● Act as a data partner to department heads for decision support and performance tracking.

6. Documentation & Compliance

● Maintain complete and updated documentation for MIS processes, reports, data models,

automation logic, and system changes.

● Ensure compliance with company data governance policies and applicable data protection standards.

System Development & Data Structuring Responsibilities

● Study and understand departmental workflows to evaluate how data is generated, processed, and utilized.

● Review existing manual and digital data systems to identify operational gaps, risks, and

mprovement opportunities.

● Recommend structured data models, reporting formats, and storage solutions aligned with

business requirements.

● Coordinate with department heads to define data structures, access levels, and reporting

standards.

● Implement new or upgraded data systems (Excel-based models, cloud platforms, ERP

integrations) with minimal operational disruption.

● Design structured data formats and role-based access controls to ensure secure and

organized data management.

● Train employees on newly implemented systems and provide post-implementation

support.

● Monitor system performance, resolve issues, and continuously improve systems based on

user feedback and organizational growth.

Qualifications & Experience

● Bachelor’s degree in Commerce, Statistics, Computer Applications, or a related field.

● 3–5 years of experience in data analytics, reporting, or system automation roles.

● Strong analytical thinking, logical reasoning, and problem-solving abilities.

● High attention to detail with excellent organizational and documentation skills.

Technical Skills (Must Have)

● Advanced Excel: Power Query, Power Pivot, VBA basics, Macros, Charts

● BI Tools: Power BI or Tableau (DAX, data modeling, dashboard optimization)

● Google Apps Script: Automation in Google Sheets, custom functions, triggers, API integrations, workflow optimization

● Vlook

Preferred Skills

● Strong verbal and written communication skills

● Ability to handle sensitive and confidential information responsibly

● Quick learner with a tech-savvy, improvement-driven, and solution-oriented mindset

Strong Data Analyst Profile with Python, SQL, and data-pipeline expertise, ideally with retail / e-commerce datasets

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Mandatory (Experience 1): Must have 4+ years of hands-on data-analyst experience, working with large datasets across areas such as sales, inventory, pricing, promotions, and customer behaviour.

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Mandatory (Tech skill 1): Must have strong hands-on Python for data processing, transformation, automation, and analysis

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Mandatory (Tech skill 2): Must have strong SQL skills, including complex queries, joins, aggregations, and data validation.

5

Mandatory (Tech skill 3): Must have a solid understanding of Data Lake architecture, ingestion, storage, and processing workflows

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Mandatory (Tech skill 4): Must have hands-on experience with ETL / ELT pipelines, data integration, and transformation

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Mandatory (Tech skill 5): Must have experience with Azure or GCP data services.

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Mandatory (Tech skill 6): Must have understanding of or experience with Databricks and distributed data processing.

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Mandatory (Tech skill 7): Must have knowledge of data modeling, data warehousing, and Lakehouse architecture

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Mandatory (Tech skill 8): Must have experience with Power BI, Tableau, or Excel for reporting.

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Mandatory (Communication): Must have strong analytical, documentation, and stakeholder-communication skills.

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Mandatory (Education): Must hold a Bachelor’s degree in Computer Science, IT, Engineering, or a related field.

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Mandatory (Note1): This is a 6-month contract role. Candidate must be comfortable with the contract engagement and able to join immediately (urgent — onboarding within ~a week)

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.

About the Role

We are looking for enthusiastic Data Analytics Interns to join our team remotely for a 3-month internship. This is an excellent opportunity to gain hands-on experience in data analysis, data visualization, database management, and reporting. The selected candidates will have the opportunity to work on real projects, analyze datasets, and develop their technical and analytical skills.

Responsibilities

• Assist in collecting, cleaning, organizing, and analyzing datasets.
• Work with SQL and databases for data retrieval, manipulation, and analysis.
• Use Microsoft Excel to organize data, perform calculations, and prepare reports.
• Assist in creating dashboards and visualizations using Power BI or Tableau.
• Work with Python libraries such as Pandas and NumPy for data analysis.
• Perform exploratory data analysis to identify trends, patterns, and insights.
• Prepare reports and presentations based on analytical findings.
• Assist in tracking key performance indicators (KPIs) and monitoring business data.
• Maintain accurate documentation of data analysis activities and project contributions.
• Collaborate with team members on data-related projects and analytical tasks.

Requirements

• Basic knowledge of Data Analytics and data analysis concepts.
• Understanding of SQL and database fundamentals.
• Familiarity with Microsoft Excel and data management.
• Basic knowledge of Python is preferred.
• Familiarity with data visualization tools such as Power BI or Tableau is an advantage.
• Strong analytical and problem-solving skills.
• Good research and organizational abilities.
• Attention to detail and accuracy.
• Ability to work independently in a remote environment.
• Good communication skills and eagerness to learn.

What You'll Gain

• Real-world experience in data analytics and business intelligence.
• Hands-on experience with data cleaning, analysis, and visualization.
• Exposure to industry-relevant tools such as SQL, Python, Excel, and Power BI.
• Mentorship and exposure to practical analytical processes.
• Completion Certificate at the end of the internship.
• Letter of Recommendation based on performance.
• Opportunity to strengthen your portfolio with real-world data projects.

Internship Details

• Duration: 3 months
• Location: Remote (Work from Home)
• Perks: Completion Certificate + Letter of Recommendation

Job Summary:

We are looking for a skilled Data Scientist with strong expertise in demand forecasting, predictive analytics, and emerging Generative AI technologies. The ideal candidate should have hands-on experience in machine learning, deep learning, NLP, and LLM-based solutions, along with proficiency in Python, SQL, Power BI, and advanced Excel. This role involves building scalable forecasting models and leveraging AI/GenAI to deliver actionable business insights.

Key Responsibilities:

• Develop and deploy demand forecasting models using machine learning and deep learning techniques.
• Analyze historical data to identify trends, seasonality, and demand patterns.
• Build predictive models to improve supply chain and inventory planning.
• Work with large datasets using Python and SQL for data extraction, transformation, and analysis.
• Design dashboards and reports using Power BI for business stakeholders.
• Utilize advanced Excel techniques (Pivot Tables, Power Query, formulas) for analysis and reporting.
• Build and integrate NLP-based solutions for text data analysis and insights.
• Develop and implement LLM-based applications using Generative AI frameworks.
• Design and deploy RAG (Retrieval-Augmented Generation) pipelines for intelligent data retrieval and response generation.
• Collaborate with cross-functional teams (operations, finance, product) to align forecasting and AI solutions.
• Continuously improve model accuracy and performance through experimentation and optimization.

Required Skills:

• Strong proficiency in Python (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch).
• Solid understanding of machine learning & deep learning algorithms.
• Experience in demand forecasting / time-series analysis (ARIMA, Prophet, LSTM, etc.).
• Hands-on experience with NLP techniques and libraries (NLTK, SpaCy, Transformers).
• Experience working with LLMs and Generative AI frameworks (OpenAI, Hugging Face, LangChain, etc.).
• Strong understanding of RAG architectures and vector databases (FAISS, Pinecone, etc.).
• Advanced knowledge of SQL for data manipulation.
• Hands-on experience with Power BI for visualization and reporting.
• Expertise in advanced Excel (Power Query, dashboards, data modeling).
• Strong analytical and problem-solving skills. 

Preferred Qualifications:

• Experience in supply chain, logistics, or e-commerce forecasting.
• Knowledge of cloud platforms (AWS, Azure, or GCP).
• Familiarity with data pipelines and ETL processes.
• Understanding of business metrics and KPIs related to demand planning.

About the Company: 

WebMD Health Corp., an Internet Brands Company, is the leading provider of health information services, serving patients, physicians, health care professionals, employers, and health plans through our public and private online portals, mobile platforms, and health-focused publications. The WebMD Health Network includes WebMD Health, Medscape, Jobson Healthcare Information, prIME Oncology, MediQuality, Frontline, QxMD, Vitals Consumer Services, MedicineNet, eMedicineHealth, RxList, OnHealth, Medscape Education, and other owned WebMD sites. WebMD®, Medscape®, CME Circle®, Medpulse®, eMedicine®, MedicineNet®, theheart.org®, and RxList® are among the trademarks of WebMD Health Corp. or its subsidiaries. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.

For Company details, visit our website: www.webmd.com

Experience: 3+ years

Qualification: Master's degree in Computer Science, Statistics, Mathematics, or a related field.

Work Timings: 2 PM to 11 PM IST

Position Requirement:

• Write clean and efficient code in Python and/or R for data analysis, statistical modeling, and machine learning tasks.
• Apply statistical and machine learning techniques to analyze large datasets and extract meaningful patterns, trends, and insights. Develop and implement predictive models for various business applications.
• Formulate hypothesis tests and design experiments to validate assumptions, measure the impact of changes, and optimize decision-making processes.
• Demonstrate strong SQL skills for efficient data querying and manipulation using window functions.
• Work with large datasets and ensure data quality and accuracy.
• Experience with dashboarding tools like Tableau is a plus to visualize and present data-driven insights to both technical and non-technical stakeholders.
• Ability to communicate complex technical concepts and insights in a clear and concise manner to both technical and non-technical audiences. Engage with the leadership team and clients to understand business requirements and provide data-driven solutions.
• Work with real world case studies in Data Science and a chance to implement various modelling techniques. Opportunity to independently execute and lead analytical projects and assignments.
• Help solve some challenging Healthcare related digital marketing problems globally. Transform business question into data requirements; collect and merge the data; analyse the data, link it to the business reality and present the results
• Develop predictive models and machine learning algorithms to study change in physician prescribing behaviour as well as WebMD integrated campaign response behaviour. Build analysis to understand user engagement and behaviours across various WebMD products.

Technical Skills needed:

1.Experience with data manipulation in SQL environment is a must. Experience with BigQuery/Vertica data warehousing is good to have

• Experience with statistical and data manipulation tools such as Python or R is a must 
• Need very good expertise in using Microsoft Excel, and Microsoft PowerPoint. Excellent presentation skills required
• Experience developing statistical models like hypothesis testing, regression models, classifications models, forecasting etc is a must 
• Algorithm designing and implementation skills in R or Python is required 

Requirements

• B.Tech/B.E. / MSc Statistics from premier institutes with
• Minimum 80% marks in 10th and 12th
• Minimum 75% marks in graduation
• 2-5 years of relevant work experience in analytics field
• Proven experience in data science, statistical modeling, and machine learning applications.
• Strong proficiency in Python and/or R programming languages.
• Solid understanding of statistical concepts, hypothesis testing, and experimental design
• Expertise in SQL for data querying and manipulation.
• Familiarity with dashboarding tools is a plus.
• Excellent presentation, communication and interpersonal skills.

We are looking for a detail-oriented and analytical Data Analyst to join our team. The ideal candidate will be responsible for collecting, analyzing, and interpreting data to identify trends, generate insights, and support business decision-making.

The candidate should be comfortable working with large datasets, creating reports and dashboards, and communicating findings clearly to business stakeholders.

Key Responsibilities

• Collect, clean, organize, and analyze data from multiple sources.
• Identify trends, patterns, anomalies, and business opportunities from data.
• Create dashboards, reports, and visualizations for business teams.
• Track and report key performance indicators (KPIs).
• Perform ad-hoc analysis to support business and management decisions.
• Develop and maintain automated reports where possible.
• Work with stakeholders to understand reporting and analytical requirements.
• Ensure data accuracy, consistency, and quality.
• Present analytical findings in a clear and actionable manner.
• Maintain documentation for reports, dashboards, and data processes.

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.