Job Description

- Senior Data Scientist (Time?Series Forecasting & MLOps)

Location

(Hybrid - Bangalore)

About the Role

We're searching for an experienced

Senior Data Scientist

who excels at statistical analysis, feature engineering, and end?to?end machine?learning operations. Your primary mission will be to build and productionize demand?forecasting models across thousands of SKUs, while owning the full model lifecycle--from data discovery through automated re?training and performance monitoring.

Key Responsibilities

Scope

What You'll Do

Advanced ML Algorithms

Design, train, and evaluate supervised & unsupervised models (regression, classification, clustering, uplift). Apply automated hyper?parameter optimization (Optuna, HyperOpt) and interpretability techniques (SHAP, LIME).

Data Analysis & Feature Engineering

Perform deep exploratory data analysis (EDA) to uncover patterns & anomalies. Engineer predictive features from structured, semi?structured, and unstructured data; manage feature stores (Feast). Ensure data quality through rigorous validation and automated checks.

Time?Series Forecasting (Demand)

Build hierarchical, intermittent, and multi?seasonal forecasts for thousands of SKUs. Implement traditional (ARIMA, ETS, Prophet) and deep?learning (RNN/LSTM, Temporal?Fusion Transformer) approaches. Reconcile forecasts across product/category hierarchies; quantify accuracy (MAPE, WAPE) and bias.

MLOps & Model Lifecycle

Establish model tracking & registry (MLflow, SageMaker Model Registry). Develop CI/CD pipelines for automated retraining, validation, and deployment (Airflow, Kubeflow, GitHub Actions). Monitor data & concept drift; trigger re?tuning or rollback as needed.

Statistical Analysis & Experimentation

Design and analyze A/B tests, causal inference studies, and Bayesian experiments. Provide statistically?grounded insights and recommendations to stakeholders.

Collaboration & Leadership

Translate business objectives into data?driven solutions; present findings to exec & non?tech audiences. Mentor junior data scientists, review code/notebooks, and champion best practices.

Minimum Qualifications

M.S. in Statistics

(preferred) or related field such as Applied Mathematics, Computer Science, Data Science.

5+ years

building and deploying ML models in production. Expert?level proficiency in Python (Pandas, NumPy, SciPy, scikit?learn), SQL, and Git. Demonstrated success delivering large?scale demand?forecasting or time?series solutions. Hands?on experience with MLOps tools (MLflow, Kubeflow, SageMaker, Airflow) for model tracking and automated retraining. Solid grounding in statistical inference, hypothesis testing, and experimental design.

Preferred / Nice?to?Have

Experience in supply?chain, retail, or manufacturing domains with high?granularity SKU data. Familiarity with distributed data frameworks (Spark, Dask) and cloud data warehouses (BigQuery, Snowflake). Knowledge of deep?learning libraries (PyTorch, TensorFlow) and probabilistic programming (PyMC, Stan). Strong data?visualization skills (Plotly, Dash, Tableau) for storytelling and insight communication.
Job Type: Full-time

Pay: Up to ?1,200,000.00 per year

Experience:

Expert in Python (Pandas, NumPy, SciPy, scikit learn): 4 years (Preferred) MLOps tools (MLflow, Kubeflow, SageMaker, Airflow): 4 years (Preferred) SQL, and Git: 3 years (Preferred) supply chain, or manufacturing domains with SKU data: 3 years (Preferred) Strong data visualization skills (Plotly, Dash, Tableau) : 2 years (Preferred)
Work Location: In person