Distributed Cloud | Data Scientist
Indexed description
Job Description
We are seeking a curious and analytical Data Scientist to join our several teams and projects. This role is dedicated to uncovering actionable insights, building sophisticated predictive models, and driving strategic decision-making across the business. You will be instrumental in translating complex data into clear, compelling narratives and measurable results.
Key Responsibilities:
- Design and develop statistical models and Machine Learning algorithms (e.g., classification, regression, clustering) to solve complex business problems.
- Conduct extensive data exploration, visualization, and hypothesis testing to identify trends, opportunities, and key performance indicators.
- Translate analytical findings into clear, compelling, and actionable stories for stakeholders, driving the adoption of data-driven strategies.
- Collaborate with Data Engineers to ensure the availability and quality of data required for advanced analysis and modeling efforts.
- Write clean, efficient code (primarily Python or R) for data manipulation, analysis, and model development.
- Continuously research and apply new methodologies and technologies in statistics, machine learning, and advanced analytics.
- 3+ years of professional experience as a Data Scientist, Applied Scientist, or Advanced Data Analyst.
- Mandatory proficiency in Python or R for statistical modeling and data analysis, and strong expertise in SQL.
- Solid theoretical and practical knowledge of statistical modeling, machine learning techniques, and experimental design.
- Demonstrable experience with data visualization tools (e.g., Tableau, Power BI, Matplotlib) for effective storytelling.
- Excellent analytical and critical thinking skills, with a proven ability to frame business questions as data problems.
- Advanced degree (M.S. or Ph.D.) in a quantitative field such as Statistics, Computer Science, Mathematics, or Economics is highly valued.
- Experience with Cloud platforms (AWS, GCP, or Azure) and their respective data science environments.
- Familiarity with distributed computing frameworks like Spark for handling large datasets.
- Practical experience in putting models into a production environment (MLOps).
- Knowledge of specific domain areas (e.g., finance, marketing, healthcare) relevant to our business.
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