Description :

Job Title : Senior Data Scientist

About the Role :

We are seeking a highly analytical and driven Senior Data Scientist to join our team focused on optimizing supply chain operations and maximizing inventory financial performance. This role plays a critical part in solving complex business challenges across inventory allocation, sourcing, internal transfers, network simulation, product lifecycle modeling, pricing, and promotional optimization.

While the immediate focus will be on inventory and pricing optimization, we are looking for a candidate with strong data science fundamentals and the ability to work across diverse domains including forecasting, operational modeling, and product strategy. The ideal candidate brings a rigorous, machine learning-driven mindset and thrives in cross-functional environments.

Key Responsibilities :

1. Modeling & Forecasting :

- Develop, deploy, and maintain predictive models for supply chain and inventory initiatives.

- Implement regression, classification, clustering, and segmentation models.

- Drive improvements in forecasting accuracy and operational decision-making.

2. Optimization & Simulation :

- Design and refine optimization models for :

- Inventory allocation

- Network design and routing

- Sourcing strategies

- Internal transfers

- Apply discrete optimization techniques (MIP, constraint solvers).

- Leverage simulation, heuristics, and metaheuristics for tradeoff analysis and scenario planning.

3. Time Series Analysis :

- Build and deploy time series models for :

- Demand forecasting

- Product performance tracking

- Lifecycle modeling

- Apply classical methods (ARIMA, Exponential Smoothing) and ML-based approaches (XGBoost, LSTM, DeepAR).

4. Exploratory Data Analysis & Feature Engineering :

- Conduct in-depth EDA and statistical analysis.

- Develop robust feature engineering pipelines.

- Identify key performance drivers and improve model robustness.

5. Cross-Functional Collaboration :

- Partner with engineering, product, and operations teams to frame business problems.

- Translate complex modeling outputs into actionable insights.

- Communicate findings effectively to both technical and non-technical stakeholders.

6. Tooling, Automation & Scalability :

- Build scalable data pipelines and decision-support systems.

- Work with Python, Spark, and cloud-based platforms.

- Ensure production-grade deployment and monitoring of models.

Required Qualifications :

Education :

- Bachelors or Masters degree in Data Science, Computer Science, Statistics, Operations Research, or related field.

Experience :

- 5+ years of experience building and deploying machine learning models in production environments.

Technical Skills :

- Strong proficiency in :

- Python (Pandas, NumPy, Scikit-learn)

- SQL

- Spark or other distributed computing frameworks

Expertise in :

- Supervised learning (regression, classification)

- Unsupervised learning (clustering, dimensionality reduction)

- Model evaluation and validation techniques

- Advanced knowledge of time series forecasting (classical and ML-based methods).

- Hands-on experience in discrete optimization (MIP, constraint solvers, genetic algorithms).

- Experience with simulation modeling and tradeoff analysis.

- Familiarity with data visualization tools (Superset, Tableau, or similar).

Key Competencies :

- Strong analytical and problem-solving skills.

- Ability to translate complex quantitative insights into business impact.

- Excellent communication and stakeholder management skills.

- High ownership mindset with the ability to work independently.

- Collaborative and adaptable in cross-functional environments.

Preferred Qualifications (Nice to Have) :

- Experience in supply chain, inventory optimization, or pricing analytics.

- Exposure to cloud platforms (AWS, GCP, Azure).

- Experience deploying ML models via APIs or production pipelines.