Senior Data Scientist

Haber; Pune District; On-site; INR 4,000,000 - 7,000,000; Full time

Benefits offered by this job

Real-World Impact

Ownership and Autonomy

Leadership opportunity

Diverse Tech Stack

Fast-Track Learning

Collaborative Culture

Product-Minded Thinking

Job summary

Haber is seeking a Senior Data Scientist to lead ML systems for real-time industrial applications. You will own end-to-end ML lifecycles, deploy models in production, and guide a small team of data scientists.

You will work on time-series data from live plants, handle sensor drift, and implement robust monitoring, explainability, and retraining strategies in a fast-paced, impact-driven environment.

Qualifications

• 8–10 years of experience in Data Science and Machine Learning with real production experience.
• Bachelor’s or Master’s in Engineering, Computer Science, Data Science, Mathematics, Statistics or a related field.
• Strong Python and SQL, with Pandas, NumPy and Scikit-learn.
• Strong foundation in statistics, probability, mathematics and optimisation.
• Strong experience in time-series analysis and forecasting as core work.
• Machine learning: regression, classification, clustering, anomaly detection and ensemble models.
• Proven experience deploying and maintaining models in production, including monitoring, drift detection and retraining.
• MLOps and production tools: Docker, Git, CI/CD, MLflow, FastAPI or similar.
• AWS for deployment and monitoring – S3, Lambda, SageMaker, CloudWatch.
• Working knowledge of deep learning using PyTorch or TensorFlow, especially for time-series problems.
• Model explainability using SHAP, feature importance or similar methods.
• Experience mentoring or leading other data scientists.
• Ability to work independently and take a project from idea to production.
• Good communication skills and comfort working with people who are not data scientists.

Responsibilities

• Build AI and ML solutions for quality prediction, process optimisation, anomaly detection, forecasting, root cause analysis and decision support.
• Work with time-series sensor data from live plants and handle sensor drift, missing data, changing conditions and limited labels.
• Own the full lifecycle: data collection, cleaning, feature engineering, model building, validation, explainability, deployment and monitoring.
• Take models into production and keep them healthy - pipelines, versioning, monitoring, drift detection, retraining and rollback.
• Own the technical direction for the data science team and decide what gets built, what gets reused and what gets retired.
• Lead and mentor 2 to 4 data scientists through code reviews, design reviews and day-to-day guidance.
• Contribute to AI system design - data access layers, model services, APIs and cloud deployment on AWS.
• Build optimisation and recommendation layers that act on model output in real time, such as setpoint recommendation for plant operations.
• Work on Generative AI and Agentic AI solutions using LLMs, RAG, tool calling and knowledge graphs, as these become part of our product.
• Work closely with domain experts, product, software, cloud and engineering teams to turn industrial problems into working solutions.
• Stay current with research in machine learning, signal processing and industrial optimisation.

Skills

Python

SQL

Time-series

ML/DS

Production deployment

MLOps

AWS

PyTorch / TensorFlow

Explainability

Leadership

Education

Bachelor's or Master's in Engineering, CS, Data Science, Math or Stats

Tools

Docker

Git

CI/CD

MLflow

FastAPI

SageMaker / Cloud tooling

Job description

At Haber, we are changing how industries run by combining automation with intelligent decision making. As a

Senior Data Scientist, you will build and own the machine learning systems behind our products - Elixa, Kaiznn,

Mount Fuji and our upcoming Fibre Morphology solution.

You will work with time-series sensor data coming from live plants, and build models for quality prediction,

process optimisation, anomaly detection, forecasting and root cause analysis. You will also lead a small team of 2

to 4 data scientists and set the technical direction for how we build and deploy models.

This is a hands-on leadership role. Our models do not stay in notebooks. They run in operating plants, where a

wrong prediction costs real money, water and energy. We are looking for someone who has built a model, put it in

production, seen it degrade, and fixed the system behind it.

Key Responsibilities

• Build AI and ML solutions for quality prediction, process optimisation, anomaly detection, forecasting, root cause analysis and decision support
• Work with time-series sensor data from live plants, and handle the real problems that come with it - sensor drift, missing data, changing conditions and limited labels
• Own the full lifecycle: data collection, cleaning, feature engineering, model building, validation, explainability, deployment and monitoring
• Take models into production and keep them healthy - pipelines, versioning, monitoring, drift detection, retraining and rollback
• Own the technical direction for the data science team and decide what gets built, what gets reused and what gets retired
• Lead and mentor 2 to 4 data scientists through code reviews, design reviews and day-to-day guidance
• Contribute to AI system design - data access layers, model services, APIs and cloud deployment on AWS
• Build optimisation and recommendation layers that act on model output in real time, such as setpoint recommendation for plant operations
• Work on Generative AI and Agentic AI solutions using LLMs, RAG, tool calling and knowledge graphs, as these become part of our product
• Work closely with domain experts, product, software, cloud and engineering teams to turn industrial problems into working solutions
• Stay current with research in machine learning, signal processing and industrial optimisation

Must Have

• 8 to 10 years of experience in Data Science and Machine Learning, with real production experience
• Bachelors or Masters in Engineering, Computer Science, Data Science, Mathematics, Statistics or a related field
• Strong Python and SQL, with Pandas, NumPy and Scikit-learn
• Strong foundation in statistics, probability, mathematics and optimisation
• Strong experience in time-series analysis and forecasting - this is the core of our work
• Machine learning: regression, classification, clustering, anomaly detection and ensemble models
• Proven experience deploying and maintaining models in production, including monitoring, drift detection and retraining
• MLOps and production tools: Docker, Git, CI/CD, MLflow, FastAPI or similar
• AWS for deployment and monitoring - S3, Lambda, SageMaker, CloudWatch
• Working knowledge of deep learning using PyTorch or TensorFlow, especially for time-series problems
• Model explainability using SHAP, feature importance or similar methods
• Experience mentoring or leading other data scientists
• Ability to work independently and take a project from idea to production
• Good communication skills and comfort working with people who are not data scientists

Good to Have

• Experience in manufacturing, chemicals, pulp and paper, water, energy, pharma, metals or similar industrial and process environments
• Generative AI: LLMs, prompt engineering, RAG, embeddings and vector databases
• Agentic AI: multi-agent systems, tool and function calling, and Model Context Protocol (MCP)
• LangGraph, LangChain or similar frameworks; Knowledge Graphs or Neo4j
• AWS Bedrock and RDS or Vision Transformers
• Optimisation, control theory, reinforcement learning or operations research
• Signal processing fundamentals
• Causal analysis, industrial digital twins, or AI governance and ISO 42001 exposure
• Experience building system design, APIs and microservices for production AI systems

What We Offer

• Real-World Impact: Your models will run in operating plants, not just in experimental labs
• Ownership and Autonomy: Freedom to explore, decide and build in a high-trust environment
• Leadership: A small team from day one, with room to grow into wider data science leadership
• Diverse Tech Stack: Time-series, sensor data, vision, optimisation and generative AI in one place
• Fast-Track Learning: A startup where learning never stops and innovation is constant
• Collaborative Culture: A team that supports initiative, experimentation and personal growth
• Product-Minded Thinking: Your work will be closely tied to product and business decisions