Marta Wójcik
Senior Data Scientist
Wrocław, Poland · Europe/Warsaw (UTC+1) · 11 years of experience
Marta focuses on turning messy data into tested models and useful decisions.
Request a shortlistWhy this profile fits
Decision-first problem framing
Starts with the decision, target metric, and data limits before choosing a model or analytical method.
Validated modeling
Uses holdouts, experiment design, and error analysis to separate a useful signal from a convincing notebook.
Clear operational handoff
Documents assumptions, monitoring needs, and the path from analysis to a decision or production workflow.
Relevant toolkit
PythonSQL
scikit-learn
PyTorch
dbtDelivery evidence
Selected work
Uplift-Based Retention Engine
Subscription streaming platformReplaced a blanket save-offer program with a two-model uplift framework that targeted only persuadable subscribers rather than everyone showing risk. Cut retention discount spend by 31% while lifting treated-cohort save rate by 4.2 points, validated through a 6-week randomized holdout before any rollout.
Churn Early-Warning Scoring Pipeline
Digital telecom operatorBuilt a daily survival-analysis pipeline scoring tens of millions of prepaid and postpaid subscribers for lapse risk. Predictions fed CRM journeys and outbound call lists, reducing 90-day voluntary churn by 18% in the highest-risk decile within two quarters.
Relevant experience
Career timeline
Senior Data Scientist, Retention & Lifecycle
Mar 2021–PresentDevlyn (remote client engagements) · Wrocław, Poland (Remote)
- Led churn and retention modeling for a rotating portfolio of subscription, fintech, and streaming clients, shipping uplift and survival models that lifted net revenue retention by 3 to 6 points per engagement.
Senior Data Scientist
Jun 2018–Feb 2021Subscription streaming platform · Warsaw, Poland
- Owned the subscriber churn model for a catalog serving 3.4 million active subscribers, moving the team from a single classifier to an uplift model that treated only persuadable users.
Data Scientist
Aug 2016–May 2018Digital telecom operator · Warsaw, Poland
- Developed survival-analysis churn scoring for tens of millions of prepaid and postpaid subscribers, reducing 90-day voluntary churn 18% in the highest-risk decile.
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Skill depth
Show experience in context.
Relevant experience by skill
See the complete skill index
Languages
Libraries/APIs
Frameworks
Tools
Paradigms
Platforms
Storage
Other
Education
Formal background
Master of Science, Mathematics, Statistics specialization
University of Wrocław · 2013–2015 · Graduated with distinction
Bachelor of Science, Computer Science
Wrocław University of Science and Technology · 2010–2013
Credentials
Certifications
Google Cloud Professional Machine Learning Engineer
Google Cloud · 2023 · Certified
Databricks Certified Machine Learning Professional
Databricks · 2022 · Certified
Communication
Languages
Polish
Native
English
Fluent
German
Conversational
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