Hello All,

Greetings from ZettaMine!!!

Job Title: Salesforce DevOps Engineer P2 – Azure DevOps, SFDX & Jenkins

Location: Hyderabad, PAN India

Experience: 3 - 5 Years

Notice Period: Immediate Joiners

About the Role

We are looking for a strong Data Scientist with deep expertise in demand forecasting, machine learning, Generative AI, Python, and Retrieval-Augmented Generation (RAG) to assess the feasibility of building reliable and accurate forecasting models using rich commercial, planning, and market data.

The ideal candidate should be able to quickly understand existing methodologies, improve model performance, develop new forecasting capabilities, and translate complex business challenges into scalable analytical solutions.

The role requires a strong balance of technical expertise, experimentation, business understanding, Generative AI capabilities, and stakeholder engagement .

Key Responsibilities

1. Forecasting Model Development

- Design and implement bottom-up demand forecasting models with forecasting horizons of up to 0–5 months .
- Develop forecasting solutions across SKU, article group, category, and customer hierarchies .
- Address challenges such as intermittent demand, sparse sales history, demand volatility, and product transitions.
- Develop weekly and monthly forecasting approaches based on business requirements and data readiness.
- Evaluate, benchmark, and optimize various forecasting techniques, including:
- Statistical forecasting models
- Tree-based machine learning models
- Deep learning and transformer-based approaches
- Ensemble forecasting methods
- Explore simulation and reinforcement learning techniques for scenario modelling.

2. Generative AI & RAG

- Develop and implement Generative AI solutions using Python and modern AI/ML frameworks.
- Design and implement RAG-based solutions for retrieving and contextualizing relevant business and forecasting information.
- Work with LLMs to generate natural-language explanations and insights from forecasting outputs.




- Develop AI-powered capabilities to support business decision-making and analytical workflows.
- Integrate structured and unstructured data sources into GenAI and RAG solutions.
- Evaluate the accuracy, relevance, reliability, and performance of GenAI/RAG implementations.

3. Data Understanding & Feature Engineering

- Analyze and engineer features from diverse commercial and planning datasets, including:
- Sell-out / POS data
- Sell-in data
- Stock-on-hand and weeks-of-stock
- Promotion and event calendars
- Product lifecycle data including NPI/PIPO, phase-in, and phase-out
- Planning inputs from Excel and BI platforms
- Market and competitive insights
- Identify leading indicators and demand drivers that improve forecast accuracy.
- Assess data quality, reliability, and business relevance of forecasting signals.
- Develop scalable data preparation and feature engineering pipelines using Python.

4. Evaluation, Validation & Performance Management

- Measure forecasting performance using business-aligned metrics such as:
- Forecast Accuracy
- Forecast Bias
- Weighted Accuracy Metrics
- Conduct rigorous back-testing and benchmarking against existing planning forecasts.
- Ensure models meet or exceed planner-generated forecast performance.
- Apply robust validation techniques to prevent overfitting and maintain model generalizability.
- Evaluate the performance of machine learning, deep learning, GenAI, and RAG solutions.

5. Explainability & Decision Support

- Develop explainable AI capabilities using techniques such as SHAP and LIME .




- Generate business-friendly insights explaining key forecast drivers and outcomes.
- Support what-if analysis covering promotions, inventory constraints, lifecycle changes, and market events.
- Contribute to LLM-powered natural-language explanations of forecast results.
- Translate complex model outputs into explicit and actionable business insights.

6. Business Collaboration

- Partner closely with commercial planners, demand planners, market insights teams, and business stakeholders.
- Translate analytical findings into actionable business recommendations.
- Facilitate workshops, model reviews, and forecasting discussions.
- Support feasibility assessments and scaling decisions.
- Communicate technical concepts and AI/ML findings effectively to non-technical stakeholders.

Required Skills & Qualifications

- 6+ years of overall experience , with 10+ years preferred .
- Strong hands-on experience in Python, Machine Learning, Generative AI, and RAG .
- Strong experience developing forecasting and predictive analytics solutions.
- Advanced Python programming skills and experience with modern machine learning frameworks.
- Hands-on experience implementing Generative AI and RAG solutions .
- Experience working with sparse, noisy, and intermittent demand data.
- Strong expertise in time-series forecasting and hierarchical forecasting .
- Strong understanding of model evaluation, forecast accuracy measurement, and bias management.
- Hands-on experience with explainable AI techniques such as SHAP and LIME .
- Experience with deep learning, transformers, or advanced machine learning approaches.
- Strong analytical, problem-solving, and experimentation skills.
- Ability to communicate complex analytical concepts effectively to non-technical audiences.
- Strong stakeholder management and collaboration skills.

Interested candidates share your updated CV to [email protected] or WhatsApp to (phone hidden). Thanks& Regards,

Praneeth.N

📌 Senior Data Scientist (Bengaluru)
🏢 ZettaMine Labs
📍 Bengaluru