Senior Data Scientist

About the Role

We are looking for a Senior Data Scientist to join our Data & AI Team and help transform complex business problems into predictive, prescriptive, and AI-driven solutions.

In this role, you will work at the intersection of Data Science, Business Intelligence, and Artificial Intelligence, developing models and decision-support capabilities that help the organization anticipate outcomes, identify opportunities, optimize actions, and make better decisions.

We are looking for someone who goes beyond building models — someone who understands the business problem behind the model, challenges assumptions, chooses the right analytical approach, and ensures that Data Science solutions create measurable real-world impact.

Key Responsibilities

• Own Data Science projects end-to-end, from business problem formulation and data exploration to modeling, evaluation, deployment, and impact measurement
• Translate complex business challenges into well-defined statistical, Machine Learning, forecasting, recommendation, or optimization problems
• Design, develop, validate, and improve predictive and prescriptive models
• Build forecasting solutions for areas such as sales, demand, market performance, customer behavior, and other business KPIs
• Develop advanced segmentation, propensity, opportunity scoring, prioritization, and behavioral models
• Design recommendation systems and Next Best Action approaches to support better business decisions
• Develop optimization solutions for problems such as resource allocation, targeting, scheduling, prioritization, and planning
• Perform advanced feature engineering and identify meaningful signals from large and complex datasets
• Design appropriate model validation strategies, benchmarks, experiments, and performance metrics
• Apply statistical methods and experimentation to evaluate business hypotheses and measure the impact of interventions
• Apply causal inference techniques where appropriate to distinguish correlation from potential causal impact
• Develop anomaly detection approaches to identify unusual patterns, risks, opportunities, or unexpected business behavior
• Ensure models are sufficiently interpretable and explainable for their intended business use cases
• Monitor model performance, identify data/model drift, and continuously improve models after deployment
• Work closely with Data Engineers to define reliable datasets, features, and scalable data requirements for Data Science solutions
• Collaborate with Data Analysts to incorporate business context, domain knowledge, and analytical findings into modeling decisions
• Collaborate with AI Developers to productionize models and expose predictive, recommendation, scoring, and optimization capabilities to AI applications and Agents
• Integrate Data Science capabilities with AI Agents, enabling agents to move beyond information retrieval and provide predictive and prescriptive intelligence
• Evaluate AI Agent-generated analytical and quantitative outputs, identify reasoning or modeling gaps, and help improve their advanced analytical capabilities
• Research and evaluate emerging approaches in Machine Learning, Generative AI, Agentic AI, and applied Data Science for relevant business use cases
• Mentor other Data Science and analytical team members and contribute to technical standards, methodologies, code quality, and best practices
• Clearly communicate model results, assumptions, limitations, risks, and business implications to both technical and non-technical stakeholders

Requirements

Must Have

• 4+ years of experience in Data Science, Machine Learning, Advanced Analytics, or a related quantitative role
• Advanced proficiency in Python
• Strong proficiency in SQL
• Strong experience with libraries and frameworks such as pandas, NumPy, scikit-learn, XGBoost/LightGBM, or equivalent technologies
• Strong understanding of Machine Learning algorithms, including regression, classification, clustering, ensemble methods, and model selection
• Strong knowledge of statistics, probability, hypothesis testing, experimental design, and statistical inference
• Experience developing and evaluating predictive models on real-world datasets
• Strong understanding of feature engineering, model validation, hyperparameter tuning, and performance evaluation
• Experience with forecasting and time-series analysis
• Ability to translate ambiguous business challenges into structured quantitative problems
• Ability to explain complex models and analytical results to non-technical stakeholders
• Strong understanding of the trade-off between model sophistication, explainability, maintainability, and business value
• Strong problem-solving skills and ability to independently lead complex analytical projects

Nice to Have

• Experience with PyTorch, TensorFlow, or other Deep Learning frameworks
• Experience with recommendation systems, ranking, propensity modeling, and Next Best Action
• Experience with mathematical optimization or Operations Research
• Experience with causal inference and advanced experimentation
• Experience with MLOps, experiment tracking, model versioning, deployment, and monitoring
• Experience with tools such as MLflow, Weights & Biases, Docker, or similar technologies
• Experience working with large-scale or distributed data processing platforms
• Familiarity with LLMs, RAG, AI Agents, tool calling, and Agentic AI architectures
• Experience integrating Machine Learning models with AI applications or enterprise AI Agents
• Experience in pharmaceutical, healthcare, commercial, sales, market, or customer analytics

In One Sentence

We’re not looking for someone who just builds models. We’re looking for someone who turns models into better decisions.