Data Scientist

Berlin - Germany

Monthly Salary: Not Disclosed

Posted on: 5 hours ago

Vacancies: 1 Vacancy

Job Summary

Role Description

Mercor is hiring on behalf of a leading AI research lab to bring on a highly skilled Data Scientist with a Kaggle Grandmaster profile. In this role you will transform complex datasets into actionable insights high-performing models and scalable analytical workflows. You will work closely with researchers and engineers to design rigorous experiments build advanced statistical and ML models and develop data-driven frameworks to support product and research decisions.

What Youll Do

Analyze large complex datasets to uncover patterns develop insights and inform modeling direction
Build predictive models statistical analyses and machine learning pipelines across tabular time-series NLP or multimodal data
Design and implement robust validation strategies experiment frameworks and analytical methodologies
Develop automated data workflows feature pipelines and reproducible research environments
Conduct exploratory data analysis (EDA) hypothesis testing and model-driven investigations to support research and product teams
Translate modeling outcomes into clear recommendations for engineering product and leadership teams
Collaborate with ML engineers to productionize models and ensure data workflows operate reliably at scale
Present findings through well-structured dashboards reports and documentation

Qualifications

Kaggle Competitions Grandmaster or comparable achievement: top-tier rankings multiple medals or exceptional competition performance
35 years of experience in data science or applied analytics
Strong proficiency in Python and data tools (Pandas NumPy Polars scikit-learn etc.)
Experience building ML models end-to-end: feature engineering training evaluation and deployment
Solid understanding of statistical methods experiment design and causal or quasi-experimental analysis
Familiarity with modern data stacks: SQL distributed datasets dashboards and experiment tracking tools
Excellent communication skills with the ability to clearly present analytical insights

Nice to Have

Strong contributions across multiple Kaggle tracks (Notebooks Datasets Discussions Code)
Experience in an AI lab fintech product analytics or ML-focused organization
Knowledge of LLMs embeddings and modern ML techniques for text images and multimodal data
Experience working with big data ecosystems (Spark Ray Snowflake BigQuery etc.)
Familiarity with statistical modeling frameworks such as Bayesian methods or probabilistic programming

Why Join

Gain exposure to cutting-edge AI research workflows collaborating closely with data scientists ML engineers and research leaders shaping next-generation analytical systems.
Work on high-impact data science challenges while experimenting with advanced modeling strategies new analytical methods and competition-grade validation techniques.
Collaborate with world-class AI labs and technical teams operating at the frontier of forecasting experimentation tabular ML and multimodal analytics.
Flexible engagement options (30-40 hrs/week or full-time) ideal for data scientists eager to apply Kaggle-level problem-solving to real-world production analytics.
Fully remote and globally flexible work structure optimized for deep analytical work async collaboration and high-output research.

Role Description Mercor is hiring on behalf of a leading AI research lab to bring on a highly skilled Data Scientist with a Kaggle Grandmaster profile. In this role you will transform complex datasets into actionable insights high-performing models and scalable analytical workflows. You will work cl...

Role Description

What Youll Do

Analyze large complex datasets to uncover patterns develop insights and inform modeling direction
Build predictive models statistical analyses and machine learning pipelines across tabular time-series NLP or multimodal data
Design and implement robust validation strategies experiment frameworks and analytical methodologies
Develop automated data workflows feature pipelines and reproducible research environments
Conduct exploratory data analysis (EDA) hypothesis testing and model-driven investigations to support research and product teams
Translate modeling outcomes into clear recommendations for engineering product and leadership teams
Collaborate with ML engineers to productionize models and ensure data workflows operate reliably at scale
Present findings through well-structured dashboards reports and documentation

Qualifications

Kaggle Competitions Grandmaster or comparable achievement: top-tier rankings multiple medals or exceptional competition performance
35 years of experience in data science or applied analytics
Strong proficiency in Python and data tools (Pandas NumPy Polars scikit-learn etc.)
Experience building ML models end-to-end: feature engineering training evaluation and deployment
Solid understanding of statistical methods experiment design and causal or quasi-experimental analysis
Familiarity with modern data stacks: SQL distributed datasets dashboards and experiment tracking tools
Excellent communication skills with the ability to clearly present analytical insights

Nice to Have

Strong contributions across multiple Kaggle tracks (Notebooks Datasets Discussions Code)
Experience in an AI lab fintech product analytics or ML-focused organization
Knowledge of LLMs embeddings and modern ML techniques for text images and multimodal data
Experience working with big data ecosystems (Spark Ray Snowflake BigQuery etc.)
Familiarity with statistical modeling frameworks such as Bayesian methods or probabilistic programming

Why Join

Gain exposure to cutting-edge AI research workflows collaborating closely with data scientists ML engineers and research leaders shaping next-generation analytical systems.
Work on high-impact data science challenges while experimenting with advanced modeling strategies new analytical methods and competition-grade validation techniques.
Collaborate with world-class AI labs and technical teams operating at the frontier of forecasting experimentation tabular ML and multimodal analytics.
Flexible engagement options (30-40 hrs/week or full-time) ideal for data scientists eager to apply Kaggle-level problem-solving to real-world production analytics.
Fully remote and globally flexible work structure optimized for deep analytical work async collaboration and high-output research.