Job Title: Demand Forecasting Data Scientist

Experience: 6+ Years

Location: Bangalore

Job Summary

We are seeking an experienced Demand Forecasting Data Scientist with strong expertise in time series forecasting, machine learning, and supply chain analytics. The ideal candidate will have hands-on experience working with Databricks and Python, a deep understanding of demand planning use cases, and the ability to deliver scalable, production-ready forecasting solutions. This role requires a sharp analytical mindset, fast learning ability, and strong collaboration skills.

Key Responsibilities

Design, develop, and deploy demand forecasting models using time series, machine learning, and deep learning techniques.
Build end-to-end forecasting pipelines on Databricks leveraging distributed computing and scalable architectures.
Apply statistical and ML techniques such as ARIMA/SARIMA, Prophet, XGBoost, Random Forest, LSTM, GRU, TimeGPT and hybrid models.
Collaborate closely with supply chain, demand planning, and business stakeholders to translate business problems into analytical solutions and should be a able to full ownership.
Perform feature engineering using internal and external drivers (promotions, seasonality, holidays, weather, etc.).
Evaluate and improve forecast accuracy using appropriate metrics (MAPE, wMAPE, Bias).
Optimize models for performance, scalability, and interpretability in production environments.
Conduct exploratory data analysis and present actionable insights to both technical and non-technical audiences.
Contribute to best practices in coding, model governance, documentation, and MLOps workflows.
Mentor junior team members and actively participate as a strong team player in cross-functional initiatives.

Required Skills & Qualifications

Technical Skills

6 years of experience as a Data Scientist with a strong focus on demand forecasting or supply chain analytics.
Strong hands-on experience with Python and its associated libraries:
NumPy, Pandas, SciPy
scikit-learn
statsmodels
TensorFlow / PyTorch (for DL models)
Solid experience with Time Series Forecasting techniques and real-world demand planning use cases.
Hands-on experience with Databricks, including Spark, notebooks, and distributed data processing.
Practical exposure to Machine Learning and Deep Learning model development and deployment.
Experience working with large, complex datasets in cloud-based or big data environments.
Practical understanding of Nixtla library is a plus.
Experience with MLOps, model monitoring, and CI/CD pipelines.

Domain Knowledge

Strong Supply Chain domain knowledge, especially demand forecasting, inventory planning, and sales forecasting.
Understanding of forecast lifecycle, demand variability, and business constraints.

Soft Skills & Competencies

Sharp analytical mindset with strong problem-solving skills.
Ability to pick up new concepts and tools quickly in a fast-paced environment.
Strong communication skills with the ability to explain complex models in simple terms.
Highly collaborative with a strong team-player attitude.
Proactive, self-driven, and ownership-oriented approach to work.

Preferred (Good to Have)

Exposure to cloud platforms (Azure / AWS / GCP).
Experience working in Agile or cross-functional delivery teams.
Prior experience in retail, CPG, manufacturing, or e-commerce domains.