{"id":3428584,"url":"https://alion.io/job/adsquare-staff-data-scientist-mfd","title":"Staff Data Scientist (m/f/d)","company":{"id":173186,"name":"adsquare","domain":"adsquare.com","url":"https://alion.io/company/adsquare","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Personio","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Berlin, Germany"],"countries":["DE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":83000,"max_usd":198000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":781},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Claude Code","optional":false},{"name":"Cortex","optional":false},{"name":"Embeddings","optional":false},{"name":"Google BigQuery","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Time Series","optional":false},{"name":"Trino","optional":false},{"name":"Amazon EKS","optional":true},{"name":"Amazon S3","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"dbt","optional":true},{"name":"Docker","optional":true},{"name":"FastAPI","optional":true},{"name":"Keras","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLM","optional":true},{"name":"LoRA","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"PEFT","optional":true},{"name":"Prometheus","optional":true},{"name":"PyTorch","optional":true},{"name":"Scikit-learn","optional":true},{"name":"Streamlit","optional":true},{"name":"TensorFlow","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-10-28T14:18:54Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-11-02T22:01:34Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"Intro\nAdsquare does Location Intelligence for programmatic advertisers. We solve hard problems with good people, good technology and a lot of data.\nOur core values guide how we work, and we hire for them:\nDrive: we turn ambition into action that produces valuable outcomes.\n\nResilience: we adapt, persevere, and grow stronger through challenges.\n\nNo BS: we are honest, transparent, and clear.\n\nHumble: we choose modesty over vanity and let results speak.\n\nMoral Compass: we act with fairness, integrity and respect.\n\nAbout the team\nData and evidence drive every decision we make. A claim that has not been tested on data it has never seen is a hypothesis, not a result. In practice, that means:\nBusiness need first. Every algorithm starts with agreed-upon business-relevant success metrics before work begins.\n\nPre-registration. Hypotheses, primary metrics, analysis plan, and stopping rules are written down before the data is looked at.\n\nStatistical power. Sample sizes and minimum detectable effects are calculated up front. We do not run experiments that cannot answer their question. And we aim to build ML models with appropriate sensitivity, specificity, and precision.\n\nIndependent tests, honest uncertainty. Every claim is tested on held-out data never used for model selection, and reported with intervals and its assumptions stated.\n\nReproducibility. Code, data versions, and seeds are recorded, so anyone, human or agent, can re-run the result.\n\nNegative results count. A well-run experiment that kills an idea is a success.\n\nYou will own this standard, teach it, and raise it.\nYour Mission\nYou will be the scientific conscience of our data products: the person who turns billions of location signals into models that work in production and can prove that they work.\nYou own the data science of a whole domain, from the business question to the live model and its measured impact. You set the methodological standard for the team, review the work of other data scientists, and decide with Product which questions are worth answering and formulate answers always with the necessary scientific evidence.\nYou will work in the Data Science team, which is part of the Data Department, alongside other data scientists and ML engineers. Using geospatial data and audience attribute data, your tasks include developing and evaluating algorithms for causal impact measurement. We ask questions like: how much did a campaign change real-world visits, which audiences does a place attract, what will footfall look like next month, and how can AI help to answer these questions faster and better.\nYour Profile\nExperience: 7+ years of hands-on data science, or 4+ years after a PhD, with several models you designed running in production and a measured business effect you can name.\n\nMathematical depth: you know the maths behind the methods you use: probability theory, inferential statistics, linear algebra, optimisation and experimental design. You can derive an estimator, spot a violated assumption, and explain why a model fails.\n\nScientific rigour: you work by the scientific method. You have designed pre-registered experiments, done power analyses, built leakage-free validation schemes, and changed your mind when the data said so.\n\nCausal inference: proven experience estimating causal impact from observational and experimental data, for example synthetic control, Bayesian structural time series, Geo-Lift, difference-in-differences, or uplift modelling.\n\nCore ML toolkit: production experience with classifiers, vector embeddings and similarity search, and collaborative filtering or other recommender methods.\n\nAgentic coding (must-have): you work daily with coding agents such as Claude Code or Cortex, use them to run data science experiments, and know how to check and review their output.\n\nLarge, messy data: you have worked with TB-scale, complex data in SQL and Python (for example, Athena/Trino, Spark, BigQuery, or Snowflake) and know how to make it fast and correct.\n\nProduction mindset: Python at a professional level (typed, tested, reviewed), plus experience deploying and monitoring models in a cloud environment, ideally AWS.\n\nCommunication: excellent scientific writing, and the ability to explain a complex algorithm to a business stakeholder so they can decide on it.\n\nBusiness acumen: you link model metrics to revenue, cost, and customer outcomes, and you know when a simpler model is the better business choice.\n\nValues: you live Drive, Resilience, No BS, Humble, and Moral Compass, and you care about privacy when working with location data.\n\nWhat you will do\nBuild and ship models that answer business questions. Design, validate, and deploy models for causal campaign measurement, audience classification, visitation forecasting, place and audience embeddings, and recommendation. Every model starts from a business need and ends as a monitored production service.\n\nMeasure causal impact. Own methods that estimate what advertising actually changed: counterfactual and synthetic-control models, Bayesian structural time series, difference-in-differences, and uplift models. Quantify the uncertainty and state the assumptions.\n\nForecast. Build time series models for footfall, reach and audience size, from hierarchical Bayesian models to neural forecasters, and backtest them honestly.\n\nWork with representations. Learn vector embeddings of places, devices and audiences; use them for similarity search, collaborative filtering and lookalike modelling.\n\nPut LLMs to work on our data. Fine-tune and evaluate LLMs for our own use cases (classifying places and audiences, extracting structure from text, agentic data workflows), with proper evaluation sets, not anecdotes.\n\nRun science with agents. Use coding agents such as Claude Code or Cortex to write analysis code, run experiment grids and draft reports, and design the guardrails that keep agent-run experiments reproducible and correct.\n\nSet the standard. Write the team's methodological playbook: experiment design, power analysis, validation protocols, review checklists. Review other data scientists' work rigorously and fairly, and mentor senior and mid-level colleagues.\n\nShape the roadmap. Think 12 to 24 months ahead. Bring state-of-the-art research into our products, identify high-value R&D opportunities, and lead solutions that span several projects.\n\nExplain it. Write clear technical papers and internal reports, and explain complex algorithms to Product, Sales, and leadership in terms of decisions, risks, and money. Represent Adsquare in peer-reviewed publications and at conferences.\n\nNice to Have\nNone of these is required on its own, but the more you bring, the better the fit.\nGeo-spatial data science (very desirable): hands-on work with H3 or S2 grids, spatial joins, coordinate reference systems and mobility data, and predictive models on top of them, such as footfall attribution, visitation prediction or place clustering.\n\nBayesian statistics: hierarchical models, prior and posterior predictive checks, and tools such as PyMC, Stan or NumPyro.\n\nTime series forecasting: state-space and structural models, hierarchical forecasting, and neural forecasters, with honest backtesting.\n\nNeural networks: you design and train deep models in PyTorch / TensorFlow / Keras and understand why they work, including representation learning.\n\nLLM-based data science: you have built LLM solutions on real data and fine-tuned models (for example, LoRA or other parameter-efficient methods) for a specific use case, with an evaluation set that proves the gain.\n\nEducation: a Master's degree, ideally a PhD, in mathematics, physics, statistics, or another natural science or quantitative field.\n\nPublications: first-author or significant contributions to peer-reviewed scientific publications.\n\nDomain: experience in programmatic advertising, ad measurement, or location data.\n\nOur Software Stack\nLanguages: Python, SQL\n\nData and cloud: AWS (Athena, S3 with Iceberg tables, SageMaker, Bedrock, EKS), Snowflake, dbt, Docker, Terraform\n\nScience and ML: scikit-learn, PyMC, FastAPI, Streamlit, Keras, etc.\n\nAI tooling: Claude Code and other coding agents, LLM APIs, MCP servers\n\nWhy us?\nOn top of a competitive package:\nHybrid work, and up to 3 months a year working remotely from anywhere\n\nA personal learning budget of 1,200 EUR a year\n\n30 vacation days a year\n\nUrban Sports Club membership, company pension scheme and relocation / home office budget\n\nRegular team and company events organised by our People team\n\nLatest hardware and all tools you need to thrive\n\nSalary Range (Annual On-Target Earnings)\n Annual On-Target Earnings (base + bonus) - 92,000 - 107,000Recruiting Process\nSome steps may take place on site if you're based in Berlin.\nTake at home quizzes\n\nIntroductory call and Values-based interview (30 min)\n\nTech-Screening Call (30 min)\n\nPractical take-home challenge: a data science problem on realistic data, solved with your usual tools, coding agents included (48h take at home)\n\nDeep-dive technical interview with the Data team: we discuss your take-home challenge solution, further questions about statistics, ML fundamentals, AI, coding (90 min)\n\nMeet the team and short research presentation: you present a piece of your own work (a paper, thesis, or project) and defend its methods and evidence (30 min)\n\nWork model\nHybridDesired start date\nJanuary 2027","description_format":"text","description_chars":9378,"description_truncated":false,"requirements":{"experience_years_min":7,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":true},"security_clearance":false,"languages":[]},"benefits":["Company events","Home office","Hybrid work"],"hiring_locations":[{"name":"Germany","iso":"DE","kind":"country"}],"hiring_excludes":[],"relocation_offered":true,"industries":["Advertising","Customer Data Platforms (CDP) & Reverse ETL"],"lifecycle":[{"event":"open","at":"2026-10-28T23:23:42Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":4,"expected_fill_days":63,"reasons":["conf:1","win:early"],"computed_at":"2026-11-02T06:13:15Z"},"pay":null,"html_url":"https://alion.io/job/adsquare-staff-data-scientist-mfd","json_url":"https://alion.io/job/adsquare-staff-data-scientist-mfd.json","meta":{"generated_at":"2026-11-03T02:05:47Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":2754,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-04T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":173186},"rest":"https://alion.io/mcp/rest/get_company?id=173186"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fadsquare-staff-data-scientist-mfd"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fadsquare-staff-data-scientist-mfd"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fadsquare-staff-data-scientist-mfd"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/adsquare-staff-data-scientist-mfd\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fadsquare-staff-data-scientist-mfd"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3428584}},{"tool":"search_jobs","arguments":{"company":"adsquare.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3428584},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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