Senior Data Scientist

Unikie; Tampere; On-site; EUR 70,000 - 100,000; Full time

Job summary

Unikie in Tampere, Finland is seeking a Senior Data Scientist to turn industrial data into operational decisions across heavy industry, chemicals, manufacturing, logistics and energy. You'll lead real production data science work beyond just models or dashboards.

The role emphasizes deciding what to predict, integrating data, and delivering actionable outputs. You will work with modern AI tooling and collaborate with stakeholders to drive value.

Qualifications

• 5+ years in data science with industrial/process use cases.
• Ability to read production data in process context.
• Structured, methodical approach before modelling.
• Strong Python and SQL with time-series analytics.
• Experience with anomaly detection, clustering and deep learning for sensor data.
• Familiarity with cloud platforms (AWS or Azure).
• Bonus: optimization (LP/MIP), R, and manufacturing clients.

Responsibilities

• Assess data sources, quality and gaps to determine what is realistically predictable - and demand the right enrichments and history before modelling.
• Predict machine failure and maintenance needs (RUL, lifecycle), and optimize production efficiency, energy use and operational flows.
• Combine predictive models with optimization to turn forecasts into prioritized actions - an approach we call Composite AI: the model predicts, optimization decides under real constraints, and the output is a decision, not just a score.
• Scope problems with clients using a structured method (CRISP-DM or equivalent) - business and data understanding before modelling.
• Assess feasibility: define use cases, KPIs and a high-level business case, and recommend the data, instrumentation or process changes needed.
• Build interpretable models that support diagnostics and operational decisions.
• Work hands-on with modern AI tooling and agentic workflows - we expect our data scientists to use the best tools, not just talk about them.

Skills

Python

SQL

Time-series analytics

Production deployment

Anomaly detection

Clustering

Deep learning

Communicative skills

Cloud platforms

Optimization

Tools

AWS

Azure

CP Optimizer

CPLEX

Job description

WE ARE LOOKING FOR Senior Data Scientist

We're looking for a Senior Data Scientist who turns industrial data into operational decisions - not just models, and not just dashboards. You'll lead data science work in real production environments across heavy industry, chemicals, manufacturing, logistics and energy. The hard part here isn't fitting a model: it's figuring out what to predict, from what data, and whether it's worth doing at all. This is the role for someone who has seen projects stall because the team modelled whatever data happened to be available - and wants to solve that properly.

What You'll Do

• Assess data sources, quality and gaps to determine what is realistically predictable - and demand the right enrichments and history before modelling
• Predict machine failure and maintenance needs (RUL, lifecycle), and optimize production efficiency, energy use and operational flows
• Combine predictive models with optimization to turn forecasts into prioritized actions - an approach we call Composite AI: the model predicts, optimization decides under real constraints, and the output is a decision, not just a score
• Scope problems with clients using a structured method (CRISP-DM or equivalent) - business and data understanding before modelling
• Assess feasibility: define use cases, KPIs and a high-level business case, and recommend the data, instrumentation or process changes needed
• Build interpretable models that support diagnostics and operational decisions
• Work hands-on with modern AI tooling and agentic workflows - we expect our data scientists to use the best tools, not just talk about them

Required Qualifications

• 5+ years in data science, ideally with industrial, manufacturing or process-industry use cases
• Process-industry data sense: you read production data in its process context, not just as statistical signals
• A structured, methodical approach you actually follow - you don't jump straight to the model
• Strong Python and SQL, solid time-series analytics, and comfort taking models toward production
• Experience with anomaly detection, clustering and deep learning for sensor data
• A communicative, proactive working style - you engage stakeholders and translate operational reality into data requirements
• Familiarity with cloud platforms (AWS or Azure)
• Bonus: mathematical optimization (LP/MIP, e.g. CPLEX / CP Optimizer), combining predictive ML with optimization, R, and prior work with manufacturing or industrial clients