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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and optimizing machine learning models, including uplift modeling and causal inference, while effectively communicating findings to non-technical stakeholders. Proficient in using tools like Snowflake and MLflow to transition models from development to production.
Highest-signal resume keywords
PythonRSQLMachine Learning Model DevelopmentStatistical Analysis
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Uplift ModelingCausal InferenceFeature EngineeringHypothesis TestingA/B TestingModel EvaluationExperiment DesignData Analysis
Soft Skills
CuriosityCommunication
Tools & Technologies
SnowflakeMLflowStreamlit
Industry Keywords
MLOpsRecommender SystemsPropensity ModelingExperimentation
Tech Stack
Tools & technologiesPythonSQL
About the role
Key responsibilities & impact- Develop and improve models behind the dynamic paywall and offers
- Work on propensity, recommender, and offer optimisation problems
- Design, run, and analyse experiments with Schibsted brands
- Report experiment results, including what they do not prove
- Apply uplift modelling and causal inference
- Take models from notebook to production
- Perform feature engineering, training, evaluation, testing, and monitoring
- Use tools such as Snowflake, MLflow, and Streamlit
- Explain findings to commercial colleagues through clear analysis and dashboards
- Contribute ideas on modelling, tooling, and MLOps
- Explore how AI can make the team’s work easier
- Collaborate with data, product, technology, business, and editorial teams
- Ask questions about business impact and profitability
Requirements
What you’ll need- Python or R and SQL, used to analyse data and build models
- Hands-on experience developing and evaluating machine learning models
- Understanding of what it takes to run machine learning models in production
- Solid foundation in statistics and experimentation, including hypothesis testing and A/B tests
- Ability to explain work to people who don’t speak ML
- Curiosity to learn from people who know the business better
- Working language: English
- CV and a few lines about a data science or machine learning project, including the problem, contribution, and learnings
Benefits
Comp & perks- Employee terms and benefits follow Schibsted’s standards in your country
- Time and space to learn, experiment and share ideas
- Experienced colleagues to learn from
- Opportunities to take ownership of your work and grow your skills
- Show n Tell and Friday data talks every other week
- Monthly hack day