Data Scientist

Role Overview

We are looking for a hands-on Data Scientist with strong expertise in Python, statistical modeling, and machine learning to develop, deploy, and scale data science solutions. The ideal candidate will have experience across the complete machine learning lifecycle, including data preprocessing, model development, deployment, MLOps, and model monitoring on AWS.

Experience in Marketing Mix Modeling (MMM) is highly preferred. Candidates with relevant experience in MarTech, AdTech, e-commerce, forecasting, optimization, or other modeling-intensive domains are also encouraged to apply.

Key Responsibilities

• Design, develop, deploy, and optimize Marketing Mix Models to measure marketing channel effectiveness, ROI, and incrementality.
• Build forecasting, simulation, and budget optimization models to support marketing strategy and scenario planning.
• Develop Python-based data pipelines for ingestion, transformation, deduplication, validation, preprocessing, and feature engineering.
• Perform exploratory data analysis (EDA), assess data quality, identify patterns, and generate actionable business insights.
• Build APIs and dashboards to present ROI, marginal ROI (MROI), channel contribution curves, and budget optimization recommendations.
• Deploy, monitor, and maintain machine learning solutions on AWS.
• Implement reproducible code, unit and integration testing, documentation, version control, and deployment best practices.
• Design and maintain ETL pipelines integrating large-scale media, sales, and business datasets.
• Collaborate with engineering, marketing, product, and leadership teams to translate model outputs into business recommendations.

Required Technical Skills

1. Python & Data Science

• Strong Python programming for backend and data science applications.
• Hands-on expertise in Pandas, NumPy, and Scikit-learn.
• Data visualization using Matplotlib, Seaborn, and Plotly.
• Proficiency with Jupyter Notebooks.

2. Statistical Modeling & Machine Learning

• Experience developing statistical and machine learning models.
• Knowledge of regression, Bayesian modeling, hierarchical modeling, and forecasting.
• Experience with model evaluation, optimization, and performance improvement.
• Ability to work with complex datasets and derive actionable insights.

3. Marketing Mix Modeling (MMM)

• Understanding of marketing effectiveness, channel contribution, ROI, and incrementality.
• Knowledge of adstock, carryover effects, nonlinear response curves, and marketing budget optimization is highly desirable.

4. SQL & Data Engineering

• Strong SQL skills, including DDL/DML, complex queries, and query optimization.
• Experience with PostgreSQL and cloud-hosted databases.
• Hands-on experience designing and maintaining ETL pipelines and integrating data from multiple sources.

5. AWS & MLOps

• Hands-on experience with AWS SageMaker, EC2, S3, Lambda, and related cloud services.
• Experience deploying, monitoring, and maintaining machine learning models.
• Understanding of MLOps practices, CI/CD, and model observability.
• Familiarity with Azure Functions is an advantage.

6. Development & Collaboration Tools

• Git/GitHub workflows, branching, pull requests, code reviews, and source-control-based deployments.
• Ability to write robust unit and integration tests.
• Strong analytical thinking, communication, collaboration, and delivery skills.

Good-to-Have Skills

• Practical experience with Marketing Mix Modeling (MMM).
• Deep learning exposure using TensorFlow, PyTorch, or Hugging Face.
• Exposure to agentic AI frameworks and advanced AI platforms.
• Experience with enterprise data pipelines and MLOps best practices.
• Full-stack exposure, including API development, UI integration, and data science platform integration.

Education

• B.Tech / B.E. in Computer Science preferred.
• BCA / MCA candidates with strong relevant technical experience may also be considered.

Ideal Candidate Profile

A hands-on Data Scientist capable of converting complex datasets into reliable machine learning solutions, statistical insights, and clear business recommendations. Strong modeling fundamentals and end-to-end ML deployment experience are essential. Direct MMM experience is a significant advantage, while candidates from adjacent modeling-intensive domains are also welcome.

Recruiter Note: Candidates should demonstrate practical Python, SQL, statistical modeling, machine learning, and AWS experience. Please highlight any direct MMM experience, including adstock, channel contribution, ROI measurement, and budget optimization, when submitting profiles.