Data Scientist V (6223)

🏢 Summary: Staff Data Scientist (Tech Lead) role focused on building and scaling real-time AI-driven decisioning systems to improve product conversion. The position combines hands-on machine learning leadership with technical ownership of production ML pipelines and experimentation strategy. You will drive advanced ML initiatives including recommendation systems, uplift modeling, and LLM-based personalization in low-latency environments. 🗂️ Requirements: 10+ years experience in Data Science with production ML systems, Expert-level Python and SQL, Strong experience with PySpark or Apache Spark, Experience building real-time ML systems with strict latency constraints, Proven experience with gradient boosting and recommendation systems, Strong background in A/B testing and causal inference, Experience building ML pipelines on AWS, Hands-on experience with LLMs including fine-tuning and evaluation, Experience with feature engineering and model monitoring in production 📃 Skills: Python, SQL, PySpark, Spark, NumPy, Pandas, Scikit-learn, XGBoost, LightGBM, AWS, Databricks, LLMs, NLP, Clustering, Uplift, Bandits, ABTesting, CausalInference 🏢 Description: We are looking for a Staff Data Scientist to join a Growth Alliance team at the Tech Lead level. This role goes beyond hands-on data science, it requires a candidate who can set technical direction, lead complex ML initiatives, and act as a strategic partner to business stakeholders. You will operate at the intersection of advanced machine learning, real-time systems, and product growth. About the Project: This is a Staff / Tech Lead position within a team driving AI-powered decisioning at scale. You will work on systems with strict real-time constraints (<40 ms latency) that directly impact end-user experience and conversion. The mission is to move the product toward AI-driven decisioning, targeting a measurable improvement in conversion (~5%). You will be expected to challenge assumptions, define the technical roadmap, and ensure that ML systems perform reliably in production. Responsibilities: Lead the design and implementation of production ML systems, including real-time decisioning and personalization engines; Drive adoption of contextual bandits, uplift modeling, and advanced recommendation frameworks; Define experimentation strategy including A/B testing, causal inference, and synthetic controls; Oversee feature engineering at scale and ensure robust model monitoring and validation practices; Act as a technical owner for decisioning platforms and Martech integrations; Collaborate with and influence senior business stakeholders to shape data science strategy; Guide and mentor other data scientists on the team; Lead GenAI and LLM-based initiatives including fine-tuning, evaluation, and content personalization. Qualifications: 10+ years of experience in Data Science, with a strong track record in production ML systems; Expert-level Python (NumPy, Pandas, Scikit-learn) and SQL; Deep experience with PySpark / Apache Spark for large-scale data processing; Mastery of gradient boosting methods (XGBoost, LightGBM), recommendation systems, NLP, clustering, and uplift modeling; Extensive experience with A/B testing, causal inference, statistical modeling, and synthetic controls; Proven ability to build and maintain production ML pipelines on cloud platforms (AWS preferred); Experience with decisioning platforms, personalization engines, and Martech integrations; Hands-on experience with LLMs, prompt engineering, evaluation, and fine-tuning; Strong stakeholder management skills with the ability to challenge assumptions and ask the right questions; Databricks experience is a nice to have. Recruitment Process: Initial interview with our recruitment team; Interview with the hiring manager; Live Coding Assessment; Meeting with the Project Manager. Inuits Sp. z o.o. is registered in the National Register of Employment Agencies (KRAZ) under number 35420.

🏢 Summary: Senior Data Scientist role focused on building and operating real-time, AI-driven decisioning systems that directly impact user experience and conversion rates. The position involves developing and deploying production-grade machine learning models with strict low-latency requirements and contributing to personalization, experimentation, and recommendation systems. It combines hands-on ML engineering with experimentation, causal analysis, and large-scale data processing in a business-facing environment. 🗂️ Requirements: 7+ years commercial experience in Data Science with production ML focus, Strong experience deploying and maintaining production ML models, Experience with real-time systems and low-latency constraints, Proficiency in Python and SQL, Hands-on experience with gradient boosting methods, Experience with recommendation systems and uplift modeling, Strong knowledge of A/B testing and causal inference, Experience building feature engineering pipelines at scale, Experience with Apache Spark or PySpark, Cloud platform experience (AWS preferred), Familiarity with LLMs and GenAI for personalization 📃 Skills: Python, SQL, NumPy, Pandas, Scikit-learn, XGBoost, LightGBM, PySpark, Spark, AWS, Databricks, NLP, LLM, GenAI 🏢 Description: We are looking for a Senior Data Scientist to join a Growth Alliance team focused on AI-driven decisioning and user-facing machine learning systems. This role is suited for a technically strong data scientist who is equally comfortable working with business stakeholders and operating production ML systems at scale. About the Project: This is a high-impact role working directly on systems that influence end-user experience and conversion. You will operate in a real-time environment with strict low-latency requirements (<40 ms), contributing to a shift toward AI-driven decisioning aimed at improving product performance and increasing conversion rates. The work spans production ML pipelines, experimentation, and personalization, not just research or experimentation in isolation. Responsibilities: Design, develop, and deploy production machine learning models with real-time constraints; Build and maintain recommendation systems, uplift models, and contextual bandit frameworks; Conduct A/B testing, causal inference analyses, and statistical hypothesis testing; Develop and maintain feature engineering pipelines at scale using PySpark / Apache Spark; Monitor and validate deployed models to ensure performance and reliability; Collaborate closely with business stakeholders to challenge assumptions and define the right problems; Contribute to generative AI and LLM-based personalization initiatives; Work with decisioning platforms, personalization engines, and Martech integrations. Qualifications: 7+ years of commercial experience in Data Science with a strong production ML focus; Proficiency in Python (NumPy, Pandas, Scikit-learn) and SQL; Hands-on experience with gradient boosting methods (XGBoost, LightGBM); Experience with recommendation systems, clustering, NLP, and uplift modeling; Solid background in A/B testing frameworks, causal inference, and statistical modeling; Experience with feature engineering and production ML pipelines at scale; Cloud platform experience, preferably AWS; Familiarity with LLMs, prompt engineering, and GenAI for content personalization; Strong communication skills and ability to work in a highly business-facing environment; Databricks experience is a nice to have. Recruitment Process: Initial interview with our recruitment team; Interview with the hiring manager; Live Coding Assessment; Meeting with the Project Manager. Inuits Sp. z o.o. is registered in the National Register of Employment Agencies (KRAZ) under number 35420.