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Mid-level popular Data Scientist title, metro location, and broad skill requirements drive high applicant competition.
Core ML and statistics skills transfer across industries, though degree specificity increases fit sensitivity moderately.
Explicit 4+ years, Statistics degree preference, and mandatory Python/cloud/ML stack create strict shortlisting filters.
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Own end-to-end data science projects including problem definition, data exploration, model development, deployment, and monitoring in production.
Apply advanced statistics and machine learning algorithms on complex datasets to generate actionable insights for strategic decisions.
Develop scalable automated data analysis processes and interactive dashboards, collaborating with stakeholders to translate needs into technical solutions.
4+ years in data science, machine learning engineering, or similar quantitative role with proven project delivery.
Bachelor's or Master's degree in Statistics (or closely related quantitative disciplines with strong statistical foundation).
Proficiency in Python and related data science libraries (Pandas, NumPy, Scikit-learn), and experience with cloud computing platforms (AWS, Azure, GCP).
Strong statistical knowledge including experimental design, hypothesis testing, and statistical inference.
Experienced in managing full lifecycle data science projects within business environments requiring technical collaboration and cross-functional communication.
Hands-on with cloud infrastructure for data processing and deploying analytical solutions, familiar with MLOps and production model management.
Preferably has sector experience in automotive data or with big data visualization tools (Looker Studio, Power BI) and advanced cloud services like GCP BigQuery and Cloud Run.