Senior Data Scientist - Personalization & Predictions

Bloomreach; United States; Remote; USD 56,000 - 70,000; Full time

Benefits offered by this job

Remote-friendly culture

Stock options

Performance bonus

Volunteer days (5 paid)

DisConnect days

Global hubs across continents

Job summary

Bloomreach is hiring for a Senior ML Engineer on the Artificial Intelligence team. You will frame business questions, build predictive models, and drive experiments across large-scale datasets in production. You will own the model lifecycle and collaborate with ML Engineering to ensure production readiness.

The role emphasizes working in a virtual-first environment with global hubs and strong focus on experimentation, evaluation, and delivering measurable business impact.

Qualifications

• 5+ years building ML models that shipped and were used by others.
• Strong Python and SQL skills; comfortable with a warehouse environment.
• Solid grounding in classical ML and rigorous experiment design.

Responsibilities

• Frame the problem before modelling; translate business questions into measurable targets.
• Work data at scale across BigQuery and Databricks; engineer features with predictive signal.
• Build and evaluate models for predictions, contextual personalization, uplift, and incrementality.

Skills

Python programming

SQL

ML modelling

Experiment design

Cloud data platforms

English proficiency

Quantitative degree

BigQuery/Databricks

LLM evaluation

Education

Quantitative degree

Tools

BigQuery

Databricks

Job description

the company 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 the company 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 missionYou 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 directionsWe'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 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 the company:Culture:

A great deal of freedom and trust. At the company 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 the company 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 the company Glassdoor page elaborates on our stellar 4.7/5 rating. The the company 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 checkins.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 the company are considered property of the company and are not subject to payment of agency fees.

#LI-Remote