Lead - Data Scientist

• Design and implement advanced AI/ML models for business use cases, including but not limited to marketing mix modeling (MMM), churn prediction, propensity scoring, demand forecasting, and revenue/sales forecasting.
• Collaborate with cross-functional stakeholders product managers, data engineers, domain experts to translate business goals into AI/ML solutions.
• Leverage Python and modern ML/DL libraries (e.g., scikit-learn, PyTorch, statsmodels, langgraph) to process structured/unstructured data, build features, and develop scalable models.
• Lead end-to-end ML pipelines, from data ingestion and feature engineering to model training, evaluation, optimization, and deployment (including MLOps best practices).
• Conduct in-depth exploratory data analysis (EDA) and statistical investigations to extract actionable insights, validate hypotheses, and guide model development.
• Stay abreast of emerging ideas in AI/ML, and actively integrate ideas from research.
• Work across the spectrum of traditional machine learning and modern AI architectures, including the development of deep neural networks.
  

Essential Skills

• Educational Background

  - Bachelors, Masters, or Ph.D. in a quantitative discipline such as Statistics, Mathematics, Computer Science, Data Science, AIMLor related fields.

• Hands-on Experience

  - 4 6 years of experience in building and deploying statistical & ML/AI models. Preferred exposure includes use cases such as MMM, churn prediction, demand/sales forecasting, and transformer-based architectures.

• Programming Expertise

  - Strong command of Python for data science and machine learning workflows, including experience with packages like NumPy, Pandas, Scikit-learn, Optuna and deep learning frameworks such as PyTorch.

• Modular & Standards-Compliant Coding

  - Proficient in writing clean, modular, and maintainable code following PEP8 standards and software engineering best practices (including version control, documentation, and reusable components).

• ML Engineering Fundamentals

  - Proficient in both traditional machine learning algorithms and neural network architectures (e.g., feedforward, convolutional, transformer-based), with a solid grasp of data preprocessing, feature engineering, and model evaluation techniques.

• End-to-End Pipeline Exposure

  - Experience working across the entire model lifecycle from data ingestion and EDA to training, tuning, deployment, and monitoring.

• Business Communication

  - Ability to translate complex analytical results into clear, actionable insights for both technical and non-technical stakeholders.

Core Competencies Required

• Effective Communication

  - Articulation of technical concepts, results, and recommendations across diverse teams.

• Mathematical Thinking & Creativity

  - Ability to approach problems with analytical rigor and devise innovative algorithmic solutions grounded in mathematical principles.

• Passion for New Ideas & Research

  - Curiosity and enthusiasm for exploring emerging techniques, experimenting with novel approaches, and contributing to innovation in AI/ML.

• Execution-Oriented Planning

  - Ability to structure complex problems and drive solutions with a balance of speed and accuracy.

• Cross-Functional Collaboration

  - Partnering with product, engineering, marketing, sales and leadership teams to align AI solutions with business goals.