Senior Data Scientist - - Remote (based in Romania)

A small piece of paper with numbers that have the power to change a life forever! Okay... let's replace the paper with an app and one person with 500,000+ monthly users. This is our business!

Your Mission

As Senior Data Scientist (m/f/d) - Build decisioning and optimization models that measurably improve how we allocate budgets, choose placements, prioritize experiments, and personalize user communication - while meeting production-grade engineering standards (reliable pipelines, monitoring, and safe deployment). This role exists to turn data into automated or semi-automated actions across marketing and adjacent business functions.

Your Tasks & Responsibilities

• Build prediction models (e.g., conversion/CPA forecasts, propensity/LTV, churn risk, creative or placement performance signals) with strong validation and calibration.
• Build decision policies: rules + constrained optimization (and bandits where appropriate) for allocation, placements, pacing, creative rotation, and personalization.
• Design and analyze experiments/incrementality tests to validate models and policies (not just offline metrics).
• Productionize with “MLE-quality”: reproducible pipelines, versioning, monitoring, alerting, and safe rollback; partner closely with Engineering/Data.
• Translate outputs into decision-ready guidance and clear trade-offs (expected impact, uncertainty, constraints).
• Maintain concise documentation of models/policies, data dependencies, and decision logic.

Your Goals in This Role

• Within 1 month: Deliver a first working “Decisioning MVP” (one high-impact optimization use case) that produces actionable recommendations on a weekly cadence, with documented assumptions and evaluation.
• Within 2 months: Have 2 decision models/rule systems running in production-like operation (scheduled, monitored, versioned), influencing real decisions (budget/placement/creative/personalization).
• Within 3–4 months: Launch a personalization or allocation model that is validated via experimentation (holdout/A-B) and shows measurable lift vs. baseline policy.
• Within 6 months: Operate a repeatable pipeline for continuous learning (re-training/refresh + monitoring + guardrails) and ship at least 4 high-impact decisioning/optimization improvements used by teams.

Your Key Competencies

Must-have

• Strong applied ML + statistics (modeling, evaluation, calibration, leakage prevention, uncertainty-aware thinking).
• Experience building decisioning/optimization (constraints, policies, experimentation-driven iteration; bandits a plus).
• Solid engineering fundamentals: clean code, testing mindset, reproducible pipelines, monitoring/alerting, and secure handling of data/secrets.
• Strong data skills: SQL + Python, feature engineering, data QA, and working with warehouses/lakes.
• Ability to run end-to-end delivery: problem framing model/policy validation rollout measurement.

Nice-to-have

• Ads/marketing domain (DSPs/walled gardens, attribution/MMP concepts) and/or personalization (CRM/push).
• Causal inference / uplift modeling experience.

Your Soft Skills

• High ownership: ships, measures, iterates—doesn’t stop at “analysis done”.
• Strong stakeholder communication: explains trade-offs, uncertainty, and rollout risks clearly.
• Pragmatic prioritization under ambiguity; focuses on highest ROI levers first.
• Calm, structured execution (production-quality over “clever hacks”).
• Collaborative mindset with Engineering/Data/Marketing/Finance.

Your Impact in This Role

• Higher ROI and fewer wrong moves through evidence-based allocation and optimization.
• Faster learning cycles via experimentation-backed model iteration.
• Reduced manual decision work through reliable decisioning systems and guardrails.
• A scalable foundation for personalization and automation beyond marketing.

Your Background – Education & Experience

• Masters Degree in Computer Science, Data Science, Statistics, Mathematics, Econometrics, or Business Informatics (or equivalent proven skill).
• 5–10+ years experience in Data Science / Applied ML, with at least one example of models/policies used in production.
• Proven experience working with engineers on production constraints (monitoring, reliability, safe deployment).

What we offer you:

Remuneration & work-life balance:

• Competitive salary and trust-based working hours.
• Private health insurance.
• Generous training budget.
• 2 extraordinary team events (4 days) per year.
• Meal benefit.

Communication & trust:

• Open, honest and direct communication. Your ideas are welcome!
• A feedback meeting every quarter to help us grow together.
• We encourage innovation and are open to new ideas that push the boundaries.

Modern working:

• Everything you need for your daily work: MacBook, monitor, headphones and more.

Individual training:

• One training day per month and a generous training budget for your personal development.

Buddy program:

• An experienced team member will support you from day one to help you get started.

The team

• We are a colorful bunch from different nations and backgrounds. We don't distinguish by religion, gender, age, marital status... For us, the focus is on what you can do!

Our values:‍

MAKE IT HAPPEN

• Drive results - Own the problem, the goal, and the outcome through self-organization and decisions backed by data
• Speak up with courage - Challenge ideas and raise issues directly when it matters
• Getting things done - Be pragmatic, keep momentum, and bring the energy
• Deliver quality impact - Ship solutions that move our mission and strategic goals forward

WIN TOGETHER

• Communicate openly and respectfully - Be transparent, assume positive intent, and set aside own ego when interacting with others
• Help others win - Proactively share knowledge, time, and strengths in an interdisciplinary set-up
• Work across cultures - Learn from others’ perspectives and actively refine own style for trust-based collaboration in a diverse team
• Bring team spirit - Create moments of joy and belonging that fuel big outcomes together

SHAPE THE FUTURE

• Experiment to learn - Run tests, measure outcomes, treat mistakes as an invitation to learn and adapt insights into action
• Spot opportunities - Stay close to customers and market trends to identify what’s next
• Build user-first innovation - Deliver trustworthy, data-driven solutions and services that set the bar
• Grow yourself - Challenge and reflect on the way you work, seek feedback, and keep developing your skills
  

**Sound like you? https://nextlottogmbh.teamtailor.com/