Data Scientist III

OpenX is focused on unleashing the full economic potential of digital media companies. We do this by making digital advertising markets and technologies that are designed to deliver optimal value to publishers and advertisers on every ad served across all screens.

At OpenX, we have built a team that is uniquely experienced in designing and operating high-scale ad marketplaces, and we are constantly on the lookout for thoughtful, creative executors who are as fascinated as we are about finding new ways to apply a blend of market design, technical innovation, operational excellence, and empathetic partner service to the frontiers of digital advertising.

A Data Scientist III is a proficient, fully independent scientist who owns medium-to-large data science projects end-to-end — from problem formulation and research through to deploying and maintaining production models. In this role, you will build production-ready models and analyses that solve real marketplace problems, partner with product and engineering to ship them, mentor junior scientists, and act as a strong technical voice within your team.

Problems at this level include bidding and yield modeling, relevance and prediction systems at exchange scale, experimentation and causal measurement of marketplace changes, and the feature engineering, validation, and monitoring required to run ML reliably in production.

The ideal candidate brings a solid applied machine learning foundation, growing judgment in selecting methods for business problems at scale, and a track record of carrying analytical work from an ambiguous question through to measurable production impact.

Employment contract (19 550 - 21 850 PLN gross/monthly) OR B2B (160 - 179 PLN net/hourly)

Working with the newest technologies such as Cloud Computing (GCP)

Challenges at work that are difficult to find anywhere else!

Solving important problems in a scale

Joining a company that is growing and scaling

Flexible working hours & remote work option

Modeling & Technical Execution:

Own the end-to-end data science lifecycle for moderately complex models and significant project components — spanning data ingestion, feature engineering, modeling, validation, deployment, monitoring, and retraining.

Apply expertise across several core areas of machine learning and statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference concepts, experimentation design), selecting appropriate methods for complex data science problems.

Write efficient, modular, well-tested code for data processing, feature engineering, and model training/inference, leveraging distributed tooling (e.g., Vertex AI pipelines, Dataflow, BigQuery) where appropriate.

Design and implement robust validation frameworks for complex experiments and models, accounting for potential biases and real-world performance.

Troubleshoot complex model performance issues, data anomalies, and code bugs effectively with little guidance.

Execution & Collaboration:

Define analytical approaches and scope data science projects for moderately complex or ambiguous business problems.

Partner with product managers and stakeholders to define success metrics and experiment goals, and to translate marketplace problems into data science solutions.

Lead the design and analysis of experiments (e.g., A/B tests, switchback) for your projects, and interpret complex model results and experimental outcomes with a focus on actionable insights and business outcomes.

Proactively identify opportunities within your domain where data science can provide significant value, and initiate exploration.

Follow and help improve established team processes for coding standards, documentation, reproducibility, and experimentation.

Mentorship & Influence:

Mentor DS I and DS II scientists, providing technical guidance, reviewing code, analyses, and models, and supporting their growth in analytical and modeling skills.

Influence technical decisions within the team regarding modeling choices, validation strategies, and tooling through well-reasoned arguments and expertise.

Drive improvements to team standards, data science best practices, and analytical rigor; take ownership of specific team practices or technical components (e.g., a feature store component, leading experimentation reviews).

Educate stakeholders on the capabilities and limitations of data science models, and clearly explain complex methodologies and findings to both technical and non-technical audiences.

Participate actively in recruiting, providing high-quality, graded interview feedback for candidates up to this level.

B.S. or M.S. in Data Science, Machine Learning, Computer Science, Physics, Mathematics, Operations Research, or a related technical field with 5+ years of relevant industry experience; OR a Ph.D. in a related field with 2+ years of relevant experience.

Demonstrated ability to independently own the full data science lifecycle — from problem formulation and feature engineering through model deployment, monitoring, and ongoing maintenance.

Solid expertise in several core areas of machine learning and/or statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference, experimentation design), with the judgment to select appropriate methods for complex problems.

Strong foundation in probability and statistics, including techniques that scale to large datasets.

Experience designing and analyzing experiments (e.g., A/B testing) and building robust model and experiment validation frameworks.

Strong Python and SQL skills; experience with ML frameworks such as TensorFlow or PyTorch.

Ability to write efficient, modular, well-tested code and to collaborate with engineering to move models and analyses into production.

Strong communication skills, including the ability to convey complex technical concepts to both technical and non-technical audiences.

Experience developing, evaluating, or optimizing models or bidding algorithms for RTB environments.

Experience working with a cloud platform like GCP/AWS/Azure, with emphasis on GCP and the Vertex AI platform.

Experience with ML pipeline and orchestration tools such as TFX, Kubeflow, or Airflow.
Familiarity with other programming languages such as Java and Go.

Experience working in digital media, marketing technology, or advertising technology, especially in marketplace, auction, or exchange systems.

Experience supporting and improving production ML models beyond their initial deployment.

Experience mentoring junior data scientists.

Annual performance bonus

Tax-deductible system due to copyright protection: 75%

Private health care for you and your family (covered by OpenX)

Private life and travel insurance

MultiKafeteria program

Training: access to the LinkedIn learning platform and Tech workshops

Holiday Allowance

Pension scheme (PPK from PZU)

Additional paid day off

Free parking lot

Access to peer to peer recognition platform

Monthly work-from-home allowance and one-time payment when you join us to help you set up your home office

We celebrate team members' important personal milestones (vouchers, gifts)

Our five company values form a solid bedrock serving to define us as a group and guide the company. Our values remind us that how we do things often matters as much as what we do.

We are one team. There are no exceptions. We are a group of strong and diverse individuals unified by a shared mission. We embrace challenges and win together as a team. We respect and care about our colleagues and cultivate an inclusive culture

We innovate on behalf of our customers. We understand, respect, and listen carefully to our customers. We build great products to solve our customers’ problems. We manage our customers’ expectations clearly and honestly. We are a trusted partner to all of our customers - we act with integrity at all times. We care.

We are all owners of OpenX

We all have a voice to improve OpenX

We stake our personal and professional reputations on the excellence of our work

We are not interested in just "doing our jobs"; we take ownership to drive results

We understand and respect what each of us does. We are eager to teach and share what we know with others, both internally and externally. We are eager to learn from others and we ask questions internally and externally. 

We take responsible risks and own and learn from our mistakes. We recognize and repeat success. We actively seek out and provide constructive feedback. We adapt quickly and embrace change. We tackle growth and learning with real urgency. We are endlessly curious.

Our three traits capture what makes a great team member at OpenX.

Ideal team players are humble and demonstrate integrity. They put the team's success above their own, share credit generously, and value collective achievements. They are self-assured, open to coaching, and committed to continuous learning.

Ideal team players are results-driven and motivated. They are curious, always seeking more to do, learn, and take on. As proactive problem-solvers, they take initiative without needing external motivation. They continuously think about the next steps and opportunities for improvement.

Ideal team players are smart and possess the intellectual acumen to understand the complexities of our organization and industry. They are interpersonally intelligent, good communicators, and exemplify sound judgment in their interactions across the company to foster a collaborative environment.

It is a fundamental principle at OpenX not to discriminate against employees or applicants for employment on any legally-recognized basis including, but not limited to: age, race, creed, color, religion, national origin, sexual orientation, sex, disability, predisposing genetic characteristics, genetic information, military or veteran status, marital status, gender identity/transgender status, pregnancy, childbirth or related medical condition, and other protected characteristic as established by law.

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