Data Analyst

Analytics Lead – AI & Decision Intelligence

Technical Leadership | People Management | Client Engagement | AI / Data / ML

About the Role

Our client is an enterprise AI and Decision Intelligence product company building advanced solutions for banking,

financial services and insurance. Its work spans predictive analytics, Machine Learning, Generative AI and Agentic AI,

with a strong focus on production-ready deployment, explainability, governance and measurable business outcomes.

The company is looking for an experienced Analytics Lead – AI & Decision Intelligence to lead a multidisciplinary team

of Data Scientists, ML Engineers, AI Engineers and other technical specialists.

This is a technical leadership, people management and client-facing role. The successful candidate is not expected to

be a hands-on expert in every area of AI, Data Science and Machine Learning. Instead, the ideal person will bring deep

expertise in at least one core AI/Data/ML discipline, together with the technical breadth and managerial capability to

lead specialists across multiple domains.

You will provide direction to technical teams, translate business problems into AI-led solutions, drive successful

delivery, and act as a key interface between highly technical teams and clients. A critical part of the role is the ability to

simplify complex technical concepts and communicate them clearly to senior and non-technical stakeholders.

What You’ll Do

 Lead and manage a multidisciplinary team of Data Scientists, ML Engineers, AI Engineers and Analytics

professionals.

 Provide technical direction across AI, Machine Learning and Data Science initiatives while enabling specialists to

own their areas of expertise.

 Translate business and customer requirements into appropriate analytical and AI solution approaches.

 Own delivery across scope, priorities, timelines, technical quality and business outcomes.

 Review and challenge technical approaches, solution designs and architecture without needing to be the hands-on

specialist in every area.

 Mentor, develop and motivate technical team members and build a strong culture of collaboration and

accountability.

 Establish and reinforce best practices around model development, deployment, governance, monitoring and

continuous improvement.

 Work closely with Product, Engineering, Sales and Customer Success teams on solution development and product

roadmaps.

 Lead client workshops, technical discussions and solution presentations.

 Explain complex AI and analytics concepts in simple, business-friendly language to senior and non-technical

stakeholders.

 Support pre-sales, solutioning and customer-facing technical engagements.

Technical Capabilities

Candidates should have strong exposure to the broader AI and Data Science ecosystem. Relevant capabilities include:

 Machine Learning & Deep Learning

 Statistical Modeling

 Python and SQL

 Data Science frameworks such as Scikit-learn, XGBoost, LightGBM, TensorFlow or PyTorch

 Data Engineering concepts and Feature Engineering

 Predictive and Prescriptive Analytics

 Model Explainability

 MLOps, CI/CD, model deployment and monitoring

 Generative AI and Large Language Models (LLMs)

 Retrieval-Augmented Generation (RAG)

 AI Agents and Agentic AI architectures

Analytics Lead – AI & Decision Intelligence | LinkedIn Job Description

 Prompt Engineering

 Responsible AI, model governance and regulatory considerations

 Cloud platforms such as AWS, Azure or Google Cloud

Important: You do not need to be an expert in all of the above. We are looking for deep expertise in at least one core

area of AI, Data Science, Machine Learning, ML Engineering, Generative AI, MLOps or Data Engineering, combined with

sufficient breadth to effectively lead experts in adjacent areas.

What We’re Looking For

 5–8 years of overall experience in AI, Data Science, Machine Learning, Analytics, Data Engineering or a related

technology discipline.

 Proven experience leading or managing teams of Data Scientists, ML Engineers, AI Engineers or similar technical

professionals.

 Strong people management, mentoring, delegation and team-building capabilities.

 Experience leading complex technical projects involving multiple specialist disciplines.

 Strong client-facing, stakeholder management and project delivery skills.

 Ability to communicate technical concepts clearly to non-technical audiences.

 Strong presentation skills and confidence engaging senior business stakeholders.

 Ability to balance technical considerations with business priorities and delivery objectives.

 Strong problem-solving, decision-making, organisational and managerial skills.

Preferred Experience

 Experience delivering AI or analytics solutions in Banking, Financial Services or Insurance.

 Exposure to use cases such as credit risk, fraud detection, customer acquisition, marketing analytics, collections,

deposits or pricing analytics.

 Experience working in an AI product, technology consulting or enterprise analytics environment.

 Exposure to Generative AI, LLM and Agentic AI programmes.

 Understanding of Responsible AI, explainability and regulatory considerations.

Education

 Bachelor’s or Master’s degree in Engineering, Computer Science, Data Science, Statistics, Mathematics or a

related discipline.

 MBA preferred, but not essential.

The Profile We’re Looking For

We are looking for a technical leader who can lead experts — not someone expected to be the expert in everything.

Technical Depth + People Leadership + Client Management + Delivery Ownership

If you have strong expertise in one area of AI/Data/ML, have successfully managed technical teams, and can

confidently engage with both technologists and business leaders, we would like to hear from you.

Tealbox Digital · Gurgaon · Hybrid, 3 days a week · 5–10 years' experience

₹20–26 lakh fixed + up to 20% variable

Please fill the form to apply: https://forms.gle/zi11r958i7hjneRQA

The bet we're making

Most of what a media buyer does today - building campaigns, structuring accounts, reading reports, spotting the obvious optimisation - is going to be absorbed by AI. Not in five years. Now.

If that's right, the scarce skill in an agency stops being platform expertise. It becomes two things. Knowing what to point the systems at. And being the person a client actually trusts.

We're hiring for that role. You'd own a portfolio of client businesses. You'd run them through an AI layer we're building. You'd be the human accountable for what comes out. Not an account manager who uses AI. Someone whose job is to get far more done per person than the industry thinks possible.

About Tealbox

We're a paid media agency for B2B SaaS and DTC brands, on Meta, Google and YouTube. Accounts run from ₹5 lakh to ₹5 crore a month in ad spend. Small team, serious about the craft. We're working out what an agency should be once the execution gets automated.

What you'd own

• The portfolio. A book of client businesses, end to end. Measured on whether clients stay and grow and on the portfolio's margin - not on what you shipped.
• The client relationship. You're the person they call. You bring a point of view they didn't ask for. You tell them the truth when it's inconvenient. You say no when no is right.
• Quality control at scale. The hard part. Twenty accounts of AI-generated analysis means twenty accounts of plausible-looking output. Some of it is wrong. All of it is spending someone's money. You're the check.
• The AI layer, applied. We build the systems. You decide how they're used on each account. You spot where they're working and where they're producing confident nonsense. You tell us what to build next.
• A small pod. A couple of people plus the systems. You coach them and set how the pod works.

Who we're looking for

Five to ten years in account management, client servicing, performance marketing or growth. We care more about the shape of your judgment than the label on your CV.

• Business judgment. You see what actually drives a client's business. When you see a number, you have an instinct for whether it's plausible before you check.
• Client trust. You've held senior relationships and kept them. You've challenged a client and been right.
• AI fluency you can show. Not "I use ChatGPT." You've changed how you work. You know where the tools break. You've built something other people now use.
• The scepticism to catch a wrong answer. You don't accept confident output because it's well formatted.

On advertising knowledge, read carefully. You need to read performance data with confidence. You need to hold your own with a media buyer. You need to be credible with a client about why performance moved. That's a real bar and we'll test it. You do not need to build campaigns yourself. We think the buying craft moves into the systems. What doesn't move is judgment about whether the output is any good. If you're a strong media buyer who wants to keep building campaigns, this job will frustrate you.

Compensation

Fixed: ₹20–26 lakh a year, depending on experience. Variable: up to 20% of fixed, paid twice a year. Three targets: client retention in your portfolio, portfolio margin, and accounts under management.

Why this is worth your time

• You'd be early on the thing everyone's about to do. Every agency will claim to be AI-native within two years. You'd have actually done it, with proof.
• Your output is your market value. If you can run twenty-five accounts where a peer runs six, you're worth several times that peer in the market. The variable is built to reflect that. This role makes you more valuable, not more locked in.
• Direct line to the founder building the system you use. Your feedback goes straight to the person building it.

The honest version

• The AI layer runs on a few accounts today. It's being expanded. Some of what you need will be ready when you join. Some won't. You'd shape it from real problems.
• The ramp is real. You start with five to eight accounts, not twenty. Twenty to thirty is where this goes within a year, if the systems deliver. That's the direction, not the starting line.
• Gurgaon-based, hybrid, three days a week in office; days agreed with Varun.
• We work to the outcome. When a client outcome, a launch or a live problem needs attention outside normal hours, we deal with it. That expectation grows with seniority. Three things come with it. Your manager owes you recovery time after a heavy stretch. Anything that causes repeated out-of-hours work gets fixed as a process problem, not absorbed. And nobody here measures commitment by response time at midnight.
• We're mid-repair. We're a founder-led business fixing how it manages people. You'd be joining during, not after.
• Nobody has done this job before. Including us.

How we'll hire

Four rounds, quick, and you'll know where you stand at every step.

1. Track record and business judgment: A conversation.
2. Assignment: A three-hour take-home exercise.
3. Client leadership: Assignment discussion and a live client role-play.
4. Values and ways of working: A two-way conversation.

If this sounds like the kind of challenge you’d enjoy, we’d love to hear from you.

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.

Data Science Engineer – Python, SQL, Statistics & Machine Learning

Company: Talview

Experience: 3–5 years

Location: Bengaluru, Karnataka

Work Mode: Work from Office, with some flexibility

Work Week: 5 days

About Talview

Talview builds AI-powered hiring and assessment solutions for enterprise organisations. Our products support candidate screening, interview scheduling and online exam proctoring, helping teams improve efficiency and make better-informed decisions.

Role Overview

We are looking for a Data Science Engineer with strong hands-on experience in Python, SQL, statistics and classical machine learning, along with experience working on GenAI and Large Language Models (LLMs).

You will work across data processing, statistical analysis, experimentation, model development, evaluation and production AI/ML workflows. You will collaborate with Engineering and Product teams to translate business problems into measurable data science solutions.

This role requires practical experience in classical ML and statistics, beyond working only with LLM APIs or prompt engineering.

Roles and Responsibilities

• Develop complex SQL queries for data extraction, transformation, analysis and validation.
• Use Python for data processing, exploratory data analysis, automation and reusable experimentation utilities.
• Apply statistical analysis, hypothesis testing and experimental design to evaluate business and product outcomes.
• Develop and evaluate machine learning models using appropriate metrics and clearly defined success criteria.
• Build LLM workflows involving prompting, structured outputs and comparative evaluation of models and configurations.
• Create evaluation datasets, benchmarks, sampling strategies and ground-truth validation approaches.
• Perform error analysis and root-cause analysis to identify model failures, anomalies and performance degradation.
• Analyse production data and troubleshoot data, model and pipeline issues.
• Integrate data from APIs, databases and other structured and unstructured sources.
• Collaborate with Engineering and Product teams on ML pipelines, evaluation frameworks and inference workflows.
• Research and test techniques to improve AI/ML system performance.

Required Skills and Experience

• 3–5 years of relevant experience in Data Science, Machine Learning, AI or a related field.
• Strong hands-on Python programming skills.
• Strong SQL skills for complex querying, data transformation and analysis.
• Solid understanding of statistics, hypothesis testing, experiment design and performance measurement.
• Practical experience developing and evaluating classical machine learning models.
• Experience with LLMs, prompting, structured outputs and evaluation.
• Experience defining evaluation metrics, benchmarks and error-analysis approaches.
• Ability to work with APIs, databases and structured and unstructured datasets.
• Strong analytical and problem-solving skills.
• Ability to explain technical decisions, interpret results and communicate findings to stakeholders.

Preferred Skills

• Experience designing end-to-end ML or LLM workflows.
• Experience with dataset creation, sampling, annotation and ground-truth validation.
• Experience analysing production AI/ML systems and debugging model or pipeline issues.
• Experience building reusable experimentation and evaluation frameworks.

Key Skills

Python, SQL, Data Science, Statistics, Machine Learning, Classical ML, Hypothesis Testing, Experimental Design, Model Evaluation, Exploratory Data Analysis, GenAI, Large Language Models, LLM Evaluation, Error Analysis