About the Role
We're looking for a Data Scientist with strong breadth across classical data science and hands-on depth in forecasting. The ideal candidate can work across the full data science lifecycle - from problem framing and data exploration to modeling and deployment - and brings specific, demonstrable experience with time-series and demand forecasting problems.
Key Responsibilities
- Work across the full data science lifecycle - problem framing, EDA, feature engineering, modeling, validation, and deployment
- Build and maintain forecasting models (time-series and demand/sales forecasting) for client and internal use cases
- Apply classical ML techniques - regression, classification, and clustering - to a range of business problems
- Perform rigorous EDA and data quality checks to ensure reliable, production-ready datasets
- Collaborate with clients and internal stakeholders to translate business problems into data science solutions
- Communicate findings and model outputs clearly to both technical and non-technical audiences
- Support deployment and monitoring of models in production environments
- Mentor junior data scientists as needed
Required Skills & Experience
- 4-6 years of experience in data science/analytics roles with a broad, generalist skill set
- Strong, hands-on experience with forecasting - time-series methods (ARIMA, Prophet, exponential smoothing) and/or ML-based forecasting approaches
- Solid grounding in classical ML - regression, classification, clustering, and feature engineering
- Proficiency in Python and standard data science libraries (pandas, scikit-learn, statsmodels)
- Experience with SQL and working with large, real-world datasets
- Experience deploying or operationalizing models, not just notebook-level analysis
- Strong communication skills - comfortable presenting findings to clients and stakeholders
- Ability to work independently and manage ambiguous, client-facing problems
Good to Have
- Experience with cloud platforms (AWS/Azure/GCP)
- Exposure to MLOps tooling and practices
- Experience working directly with external clients
- Awareness of and interest in the latest Gen AI and agentic AI technology, and how it applies to data science workflows
- Exposure to optimization modeling (e.g., linear/mixed-integer programming, heuristics)
