Sr. Data Scientist

About Us

We don't just measure brands. We model how they grow.

At Cypien, we've built a platform that ingests, unifies, and activates data across every surface a brand touches — XML feeds, Shopify, manual imports, first-party events, transactions, behavioral signals. We turn this into something brands can actually use: segments that mean something, scores that predict something, content that responds to something.

The platform works. The data is flowing. What it needs now is someone who can see patterns where others see noise — and build the models that turn those patterns into product.

That someone is you.

What you'll actually do?

You'll sit between the data and the decisions. One day you're elbows-deep in a churn cohort, the next you're designing the feature pipeline that will feed our next scoring model into production. You won't be making dashboards for the sake of dashboards — you'll be shaping the analytical and modeling layer that our brands rely on to act.

You'll dig into customer behavior, product performance, and campaign signal, and you'll come back with answers that change how the team thinks. You'll design the architecture behind segmentation, propensity, CLV, churn, and recommendation models — from the math up to the production pipeline. You'll work side by side with engineering on event taxonomy, data quality, and the tracking layer itself, because models are only as good as what feeds them.

When something ships, you'll be the one watching it — measuring it, A/B testing it, and quietly knowing whether it's actually working before anyone else asks.

You're probably the right person if you:

• Have a data science background — not just dashboard work. You know why a model is wrong, not just that it is.
• Have 4+ years in analytics or data science, with at least 2 of them on a model that actually made it to production.
• Are fluent in Python (pandas, scikit-learn, statsmodels) and SQL — and comfortable owning a pipeline end to end.
• Know classical ML cold (regression, tree-based methods, clustering), and can wade into deep learning when the problem calls for it.
• Think in features, not just fields. Feature engineering, evaluation, A/B testing, and causal inference are part of how you reason.
• Can explain a model to a CMO and defend a methodology in front of an engineer — in the same afternoon.
• Get genuinely excited when a model output tells a story the team didn't know was there.

Must-haves on the stack:

You've worked hands-on with all of these — not "heard of," not "happy to learn":

• GA4 — event modeling, custom dimensions, debugging tracking implementations end to end.
• VWO and Optimizely — experimentation setup, results interpretation, knowing where each tool shines and where it lies to you.
• Microsoft Clarity — session replay and behavioral signal as a real input to analysis, not just a curiosity.
• Kafka — comfortable with event streams as a data source, not just batch tables.
• MySQL and PostgreSQL — fluent in both, including the parts where they disagree.

Bonus if you've:

• Worked with modern data warehouses (BigQuery, Snowflake, Redshift).
• Shipped recommendation systems, uplift models, or LTV pipelines into production.
• Used MLOps tooling (MLflow, Airflow, dbt, Vertex AI / SageMaker).
• Worked in e-commerce, growth, marketing analytics, or CRM domains.

What Cypien looks like from the inside?

Small team. Serious data. We power decision-making for brands across retail, finance, travel, and beyond — and the analytical layer is one of the next places we're investing heavily. You won't inherit a model factory; you'll help build one.

The founding team has a successful exit and two decades of combined experience in LLM development and digital marketing. Our investors include İş Portföy and Webrazzi GSFY. We move fast, we care about craft, and we believe the most interesting data problems are still ahead of us, not behind.

Location: Istanbul (hybrid) or fully remote within ±2 time zones

Language: English working environment

If you've read this and thought "that's the job I've been waiting to be hired for" — we'd like to talk.🚀