Senior Data Scientist

About Us

Senior

Key Responsibilities

• Design and implement

  advanced forecasting models

  (statistical, ML, DL) for

  large-scale datasets

  , including multi-SKU, multi-location, and multi-horizon forecasting.
• Work on

  complex supply chain datasets

  , including demand planning, inventory movement, lead times, seasonality, promotions, and external drivers.
• Build and maintain

  feedback-loop mechanisms

  with planners (model overrides, adjustments, bias monitoring) to ensure adaptive, continuously learning forecasting systems.
• Develop scalable

  data processing pipelines

  using Python and Spark/distributed frameworks to manage millions of records efficiently.
• Build NLP components (classification, extraction, embeddings) for analytics use cases where required.
• Contribute to building and integrating LLM-powered tools (prompting, retrieval, light agent workflows) for automation.
• Deploy and monitor solutions on Azure or AWS, ensuring reproducibility, version control, and observability of model performance.
• Collaborate with Power BI experts to integrate model outputs into reports and dashboards for planners and business stakeholders.
• Contribute to internal accelerators and reusable frameworks for forecasting, MLOps, and cloud-based analytics.
• Communicate analytical findings clearly with cross-functional teams — including operations, consulting, and leadership.

Required Skills

• Forecasting:

  ARIMA, ETS, Prophet, ML-based forecasting, hierarchical forecasting, causal models
• Proven experience handling

  large-scale time series datasets

  (multi-SKU, multi-region, millions of rows).
• Experience building

  adaptive/continuous learning forecasting systems

  , including planner overrides, backtesting, auto-retraining, and KPIs (MAE/MAPE/Bias).

• Distributed Computing:

  Spark (PySpark preferred) or equivalent frameworks for scaling pipelines.

• Programming:

  Python (pandas, numpy, scikit-learn, PyTorch/TensorFlow), SQL

• NLP (Foundational):

  embeddings, transformers, basic extraction/classification (deep expertise not required)

• LLM Tools:

  Basic prompt engineering and retrieval-based systems (nice-to-have)

• Deployment:

  Docker, FastAPI/Streamlit, MLflow (preferred)

• Cloud:

  Azure or AWS experience (model deployment, storage, CI/CD)
• Strong analytical mindset with the ability to translate real supply chain/business problems into ML solutions
• Comfort working in a lean, fast-paced, high-ownership consulting environment

Good to Have

• Experience with Power BI or other visualization tools
• Exposure to Ops Research / Optimization problems
• MLOps experience: monitoring, model drift detection, automated retraining
• Prior consulting experience or strong client-facing communication skills

Why Join Us

• Opportunity to work on diverse forecasting, analytics, and GenAI automation projects
• Small, high-talent team where your work directly impacts client outcomes
• Hands-on exposure to enterprise-grade forecasting systems and LLM-powered tools
• Ownership, learning, and accelerated career growth in a collaborative, non-hierarchical setup