Sr Data Scientist
Posted Updated
Description
Role Overview
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
Haber - Senior Data Scientist Page 1 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
Computer vision: image preprocessing, object detection, classification, segmentation, OpenCV, YOLO, CNNs
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
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
Skills
- Agentic AI
- AI
- Anomaly Detection
- API
- Automation
- AWS
- AWS Bedrock
- CI/CD
- Cloud
- CloudWatch
- Computer Vision
- Data Science
- Deep Learning
- Docker
- Embeddings
- FastAPI
- Feature Engineering
- Generative AI
- Git
- Lambda
- LangChain
- LangGraph
- LLM
- Machine Learning
- MCP
- Microservices
- MLflow
- MLOps
- Neo4j
- NumPy
- OpenCV
- pandas
- Prompt Engineering
- Python
- PyTorch
- RDS
- Reinforcement Learning
- Root Cause Analysis
- S3
- SageMaker
- scikit-learn
- Signal Processing
- SQL
- Statistics
- TensorFlow
- Time Series
- Transformers
- Vector Databases
- YOLO