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