[All remote jobs](https://jobicy.com/jobs.md)[![Bloomreach logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/08/60790cd5-221.png)](https://jobicy.com/company/bloomreach.md)[Bloomreach](https://jobicy.com/company/bloomreach.md) # Senior Data Scientist – Personalization & Predictions Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/bloomreach.md)ShareRemote from[Slovakia](https://jobicy.com/job-region/slovakia.md), [Czechia](https://jobicy.com/job-region/czechia.md)SalaryEUR 50,290–62,900 / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished11 Oct 2026Apply before10 Nov 2026Listing views54Application actions2Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Bloomreach is seeking a Senior Data Scientist to develop and evaluate production personalization, prediction, uplift, segmentation, and contextual-bandit models. The role centers on framing ambiguous business questions, working with behavioral data at terabyte scale in BigQuery and Databricks, and demonstrating measurable business impact through rigorous experimentation. The successful candidate will partner closely with ML Engineering for production handoffs and communicate evidence-based findings to product, engineering leadership, and customers. Candidates need 5+ years of industry ML experience, strong Python and SQL, classical ML expertise, and depth in causal inference, bandits, recommendations, information retrieval, or GenAI evaluation. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a senior, high-impact applied ML role requiring independent problem formulation, causal and experimental rigor, and work across large-scale production data. Success depends not only on modeling skill but also on proving incremental business value and creating effective engineering handoffs. ## Salary analysis Estimated compensation compared with the broader EU market for similar roles. Estimated job medianBelow market€61,123EU market range€145k–€190k0€209k AI insightThe disclosed annual base-salary midpoint is €56,595; converted approximately to USD for the analysis currency, this is about $61,123. The estimated US market base-salary range for a senior applied data scientist specializing in personalization, experimentation, causal inference, and production ML is approximately $145,000–$190,000 annually; actual US compensation can vary materially by location and equity/bonus structure. ## Core skills Skills and capabilities most closely associated with this opportunity. [Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Predictive Modeling](https://jobicy.com/jobs?search_keywords=Predictive%20Modeling.md)[Causal Inference](https://jobicy.com/jobs?search_keywords=Causal%20Inference.md)[Contextual Bandits](https://jobicy.com/jobs?search_keywords=Contextual%20Bandits.md)[A/B Testing](https://jobicy.com/jobs?search_keywords=AB%20Testing.md)[BigQuery](https://jobicy.com/jobs?search_keywords=BigQuery.md)[Databricks](https://jobicy.com/jobs?search_keywords=Databricks.md)[Personalization](https://jobicy.com/jobs?search_keywords=Personalization.md) Sample interview questionsHow would you determine whether a personalization model created incremental conversions rather than merely targeting customers who would have converted anyway?I would define the intervention and primary business metric, then use a randomized holdout or controlled A/B test whenever feasible. I would analyze intent-to-treat effects, check balance and exposure integrity, segment carefully without p-hacking, and report confidence intervals alongside business impact. If experimentation were not feasible, I would consider causal methods such as propensity adjustment or doubly robust estimation, while clearly documenting their assumptions. Describe how you would approach an ambiguous request to reduce customer churn using behavioral data. I would first clarify the decision the business wants to make, the eligible intervention, the outcome window, and the cost of false positives and false negatives. I would explore event, purchase, and engagement data for leakage-safe features; establish a simple baseline; and validate using time-based splits. I would then recommend an action policy and an experiment to assess whether outreach or personalization changes retention, rather than stopping at predictive accuracy. What metrics would you use to evaluate a contextual bandit model before and after deployment? Before deployment, I would validate logging quality, compare candidate policies through appropriate off-policy evaluation methods, and test sensitivity to propensity estimates and support limitations. After deployment, I would monitor reward, regret proxies, conversion or revenue lift, exploration rate, policy distribution drift, fairness-relevant slices, and guardrail metrics such as discount cost. A randomized baseline or holdout policy is important for measuring incremental impact safely. How do you ensure that an ML model handoff to engineering is production-ready? I provide a concise model specification covering the objective, training data, feature definitions, leakage controls, expected inputs and outputs, evaluation results, thresholds, failure modes, and monitoring requirements. I also align on latency, batch versus online scoring, retraining cadence, versioning, and rollback criteria. A walkthrough with ML Engineering and shared acceptance tests turns the handoff into a collaborative operational plan rather than a notebook transfer. When would you choose a simpler model over a more complex model such as a deep learning approach? I would choose the simpler model when it meets the product objective reliably with lower operational cost, better interpretability, faster iteration, or more stable behavior under data constraints. I compare models using leakage-safe validation and the downstream business metric, not only offline accuracy. Complexity is justified only when it provides material, reproducible incremental value that outweighs its serving, maintenance, and observability costs. Opportunity details ## About this role. Bloomreach is building the world’s premier agentic platform for personalization.We’re revolutionizing how businesses connect with their customers, building and deploying AI agents to personalize the entire customer journey. * We’re taking autonomous search mainstream, making product discovery more intuitive and conversational for customers, and more profitable for businesses. * We’re making conversational shopping a reality, connecting every shopper with tailored guidance and product expertise — available on demand, at every touchpoint in their journey. * We’re designing the future of autonomous marketing, taking the work out of workflows, and reclaiming the creative, strategic, and customer-first work marketers were always meant to do. And we’re building all of that on the intelligence of a single AI engine — Loomi — so that personalization isn’t only autonomous…it’s also consistent.From retail to financial services, hospitality to gaming, businesses use Bloomreach to drive higher growth and lasting loyalty. We power personalization for more than 1,400 global brands, including American Eagle, Sonepar, and Pandora. You’d be joining the Artificial Intelligence team. We own the algorithmic core of the platform: Predictions, Contextual Personalization, contextual bandits, autosegmentation, and the agentic workflows behind Loomi. We work with behavioural data at terabyte scale, across 1,400+ customers, in production, every day. We are currently allowing flexibility for our employees to work from anywhere for the respective region (Central & Eastern Europe) or we are happy to meet you in our offices in Bratislava (Slovakia) or Brno, Prague (Czechia) on a full-time basis. ### The mission You find the signal in how 1,400 brands’ customers actually behave — and you prove it moved a business metric. Your models decide which customers are predicted to churn, which segments form themselves, which offer a shopper sees, and whether the discount changed anything or was going to convert anyway. ### What you’ll actually do * Frame the problem before modelling it. Most of our highest-impact work arrives as a vague business question, and turning it into something measurable is the first job. * Work the data at scale. Behavioural data, product catalogues and event streams across BigQuery and Databricks — finding features that carry real predictive signal, not the ones that are easy to compute. * Build and evaluate models across Predictions and Contextual Personalization: propensity and churn, contextual bandits, autosegmentation, uplift and incrementality. * Design the evaluation, not just the model. Offline metrics, backtests, and the A/B design that decides whether this ships. You own the question “how would we know if this is worse?” * Bring methods in from outside and judge them honestly — read the literature, run the quick PoC, and tell a real result from a well-marketed one. * Hand off cleanly to ML Engineering. You own the model and the evidence; they own making it survive production. That handoff is a document and a conversation, not a notebook over a wall. * Explain your results to people who aren’t data scientists — Product, Engineering leadership, and sometimes customers. ### What success looks like after 12 months * Two models you built are in production and you can name the business metric each one moved. * A question the team was arguing about is settled, with evidence, and your writeup is what people link to. * ML Engineering describes your handoffs as easy. ### This role bends in three directions We’d rather shape it around you than the other way round. * Modelling & data science — features, experiments, and whether the number means anything. * ML product engineering — you own an ML-powered feature end to end and the ML part is what makes it interesting. * Platform & MLOps — pipelines, serving, deployment, observability, inference cost. Most people lean one way and dip into the others. This opening is centered on the first. If you’d rather own the serving path, latency and reliability, our our [Senior AI/ML Engineer](https://job-boards.greenhouse.io/bloomreach/jobs/7127780) opening (in Czechia or Slovakia) is the better fit, and applying to both is fine. ### What you’ll need * 5+ years building ML models that shipped and were used by someone other than you — in industry, not only in research or coursework. * Strong Python and genuinely strong SQL. You should be comfortable being handed a warehouse and finding your own way around it. * Solid grounding in classical ML — tree-based models, regression, classification, clustering — and the judgement to know when the simpler model is the right answer. * Real rigour on experiment design and evaluation. You know why a metric moved, and when it didn’t move for the reason everyone assumes. * Comfort on a cloud data platform — we work in GCP (BigQuery) and Databricks, but the principles transfer. * A quantitative degree, or equivalent practical depth. * Working English, written and spoken. Plus real depth in at least one of: * Causal inference and uplift modelling — separating who converts because of an intervention from who would have converted anyway. * Contextual bandits, sequential decision-making, or off-policy evaluation. * Ranking, recommendation or information retrieval. * LLM evaluation, or applied work with the GenAI stack. Also good: you use agentic coding tools daily and have a view on where they help and where they quietly don’t. We build agents for a living, and people who use them tend to have better instincts about them. #LI-KP1 The pay range actually offered will take into account a variety of potential factors considered in compensation, including but not limited to skills, qualifications, geographic location, accomplishments, experience, credentials, internal equity and business needs, and may vary from the range listed above. Base Salary Range €50.290—€62.900 EUR ### More things you’ll like about Bloomreach: ### Culture: * A great deal of freedom and trust. At Bloomreach we don’t clock in and out, and we have neither corporate rules nor long approval processes. This freedom goes hand in hand with responsibility. We are interested in results from day one. * We have defined our [5 values](https://www.bloomreach.com/en/careers?utm_source=Job-Description-Footer&utm_medium=Job-Description&utm_content=Hyperlink-1) and the 10 underlying key behaviors that we strongly believe in. We can only succeed if everyone lives these behaviors day to day. We’ve embedded them in our processes like recruitment, onboarding, feedback, personal development, performance review and internal communication. * We believe in flexible working hours to accommodate your working style. * We work virtual-first with several Bloomreach Hubs available across three continents. * We organize company events to experience the global spirit of the company and get excited about what’s ahead. * We encourage and support our employees to engage in volunteering activities – every Bloomreacher can take 5 paid days off to volunteer*. * The [Bloomreach Glassdoor page](https://www.glassdoor.com/Overview/Working-at-Bloomreach-EI_IE442167.11,21.htm) elaborates on our stellar 4.7/5 rating. The [Bloomreach Comparably page](https://www.comparably.com/companies/bloomreach) Culture score is even higher at 4.9/5 ### Personal Development: * We have a People Development Program – participating in personal development workshops on various topics run by experts from inside the company. We are continuously developing & updating competency maps for select functions. * Our resident communication coach [Ivo Večeřa](https://www.linkedin.com/in/ivo-ve%C4%8De%C5%99a-2a94291/) is available to help navigate work-related communications & decision-making challenges.* * Our managers are strongly encouraged to participate in the Leader Development Program to develop in the areas we consider essential for any leader. The program includes regular comprehensive feedback, consultations with a coach and follow-up check-ins. * Bloomreachers utilize the $1,500 professional education budget on an annual basis to purchase education products (books, courses, certifications, etc.)* ### Well-being: * The Employee Assistance Program — with counselors — is available for non-work-related challenges.* * Subscription to Calm – sleep and meditation app.* * We organize ‘DisConnect’ days where Bloomreachers globally enjoy one additional day off each quarter, allowing us to unwind together and focus on activities away from the screen with our loved ones. * We facilitate sports, yoga, and meditation opportunities for each other. * Extended parental leave up to 26 calendar weeks for Primary Caregivers.* ### Compensation: * Restricted Stock Units or Stock Options are granted depending on a team member’s role, seniority, and location.* * Everyone gets to participate in the company’s success through the company performance bonus.* * We offer an employee referral bonus of up to $3,000! * We reward & celebrate work anniversaries — Bloomversaries!* (*Subject to employment type. Interns are exempt from marked benefits, usually for the first 6 months.) Excited? Join us and transform the future of commerce experiences! If this position doesn’t suit you, but you know someone who might be a great fit, share it – we will be very grateful! Any unsolicited resumes/candidate profiles submitted through our website or to personal email accounts of employees of Bloomreach are considered property of Bloomreach and are not subject to payment of agency fees. #LI-Remote Show more [Apply now >](https://jobicy.com/jobs/155015-senior-data-scientist-personalization-predictions.md) * ![Upload CV](data:image/svg+xml;base64,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) ### Upload your resume now To unlock remote work opportunities and be discovered by global employers. 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