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Senior Data Scientist – Personalization & Predictions

AI Summary
This Senior Data Scientist role develops production machine-learning models for personalization, predictions, churn, segmentation, and offer optimization. The position works with terabyte-scale behavioral, catalog, and event data using BigQuery and Databricks. It emphasizes rigorous experimentation, causal inference, contextual bandits, model evaluation, and explaining business impact to technical and non-technical stakeholders. The successful candidate will own the model and evidence through a structured handoff to ML Engineering while working remotely within the eligible region.
Remote from
Czechia
Salary
CZK 1,470k–1,830k / yr
Employment
Full Time
Experience
Senior
Published
Apply before
12 Dec 2026
Listing views
25
Application actions
1
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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 Senior AI/ML Engineer 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

1 470 000 Kč—1 830 000 Kč CZK

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 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 elaborates on our stellar 4.7/5 rating. The Bloomreach Comparably page 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 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

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis is a senior, high-impact applied ML role requiring deep statistical judgment beyond model implementation. The candidate must independently frame ambiguous business questions, design trustworthy evaluations, and demonstrate measurable production impact.

Salary analysis

Estimated compensation compared with the broader CZK market for similar roles.

Estimated job median (per year)Market rate
CZK1,650,000
CZK market range (per year)CZK1,400k–CZK2,100k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a base salary range of CZK 1,470,000–1,830,000. The annual period is inferred because no cadence is explicitly stated and the figures align with annual compensation for a senior full-time data scientist in Czechia. The estimated market range is CZK 1,400,000–2,100,000 per year, with an employer midpoint of CZK 1,650,000 per year.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you determine whether a discount caused additional conversions rather than rewarding customers who would have converted anyway?

I would define the treatment, outcome, eligible population, and decision point, then use randomized experimentation where possible. If randomization is unavailable, I would apply a causal approach such as propensity modeling, doubly robust estimation, or uplift modeling, validate balance and assumptions, and report incremental impact with uncertainty intervals.

Describe how you would take an ambiguous personalization request and turn it into a measurable ML problem.

I would first clarify the user decision, target population, business objective, and constraints. I would then define the prediction or ranking target, success metric, baseline, offline validation approach, and online experiment design before selecting a model.

What would you evaluate before deploying a contextual bandit for offer selection?

I would assess reward definition, exploration strategy, feature availability at decision time, logging quality, safety constraints, and bias risks. I would use offline policy evaluation where appropriate, compare against a strong baseline, and launch gradually with guardrails and monitoring for business impact and distribution shifts.

How do you ensure an offline model improvement is likely to produce online business value?

I would verify that the offline metric is linked to the actual business outcome, use time-aware validation, and compare against existing production behavior. I would then define a controlled online experiment with primary and guardrail metrics, expected effect size, and stopping criteria.

What should a high-quality handoff from Data Science to ML Engineering include?

It should include the model objective, feature definitions, training and inference requirements, evaluation results, known limitations, data dependencies, monitoring expectations, and rollback criteria. I would also hold a working session with ML Engineering to resolve operational assumptions rather than treating documentation as a substitute for collaboration.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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