Senior Data Scientist - R01563617 at Brillio 2

Senior Data Scientist Primary Skills • Problem Formulation (Business problem to Data Science Problem), OKR Validation against statistical measures, Data Wrangling, Data Storytelling & Insight Generation, Problem Solving, Excel VBA, Data Curiosity, Technical Decision Making (How many iterations to go for vs when to stop iterating), Communication & Articulation: Vocal & Written, Business Acumen (Consume new domains quickly to learn through data), Design Thinking, Data Literacy Specialization • Data Science Foundation: Senior Data Scientist Job requirements • Key Responsibilities • Develop and maintain predictive models to support business decision-making • Design and implement feature engineering pipelines using diverse data sources • Perform exploratory data analysis to uncover patterns, trends, and key drivers • Build segmentation and cohort analyses to better understand customer behavior • Evaluate model performance using appropriate metrics and continuously refine approaches • Translate analytical results into clear, actionable insights for business stakeholders • Create data visualizations and dashboards to communicate findings effectively • Collaborate with engineering teams to deploy and scale models in production environments • Ensure data quality, consistency, and reproducibility across workflows ________________________________________ Required Qualifications • 5+ years of experience in Data Science, Machine Learning, or Advanced Analytics • Strong proficiency in Python (pandas, numpy, scikit-learn) • Experience with feature engineering, model development, and evaluation • Familiarity with large-scale data processing (e.g., Spark, Databricks) • Experience with SQL and data warehousing platforms • Strong understanding of statistical modeling and machine learning techniques • Experience building data visualizations (e.g., Power BI, matplotlib, seaborn) ________________________________________ Preferred Qualifications • Familiarity with cloud environments (Azure) • Exposure to end-to-end ML pipelines and MLOps practices • Experience working with business stakeholders in data-driven environments