Senior (Lead) Data Scientist
$6,200–$7,800 year
HybridHelsinki, Uusimaa, Finland
Job Summary
Develop statistical and machine-learning methods to transform large procurement datasets into trustworthy benchmarks, indexes, and predictive models. Define success metrics and experimentation plans for new data products while validating methodologies for representativeness, outlier handling, and confidence levels. Conduct exploratory data analysis to identify patterns and opportunities, then document assumptions and limitations for internal and customer use. Assess data quality, bias, and privacy risks to support AI features with statistically valid context. At the Lead level, set scientific direction, establish reusable modelling standards, and mentor contributors to ramp up the data science capability across the organization.
Required Qualifications
- A track record of turning ambiguous business questions into clear analytical problem definitions
- Designing metrics and validation approaches that connect analytical outputs to business outcomes
- Comfort working with imperfect, heterogeneous, real-world enterprise data
- Solid command of statistics — distributions, sampling, bias, variance, confidence, correlation vs. causation, outlier treatment, and uncertainty
- Strong Python skills for analysis, modelling, visualization, and prototyping
- Strong SQL skills for working with complex analytical datasets
- Experience across the ML lifecycle: problem formulation, dataset preparation, feature design, model selection, validation, metric definition, and interpretation
- A product mindset — interest in turning analytical prototypes into repeatable, scalable product capabilities rather than one-off analysis
- Strong communication skills, with the ability to explain complex methods and limitations to Product, Engineering, Sales, CSM, and leadership
- Ability to influence technical direction across teams
- A demonstrated ability to drive the data science discipline across an organization — raising standards, growing capability, and influencing direction beyond a single team
Desired Qualifications
- Experience with procurement, spend analytics, supplier data, finance analytics, pricing, or supply-chain data
- Experience with ML methods such as forecasting, clustering, anomaly detection, recommendation systems, classification, or optimization
- Experience with Azure, Synapse, notebooks, Azure ML, or similar cloud analytics environments
- Experience with privacy-preserving analytics, anonymized datasets, aggregation thresholds, or sensitive commercial data
- Experience designing A/B tests, pilots, product experiments, or customer impact measurement
- Understanding of LLM / RAG / AI assistant use cases and how structured data can safely power AI-generated insights
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