Data Analysis: Conduct advanced statistical analysis to interpret and extract meaningful insights from large datasets. Extract data into presentable reports, charts, and graphs. Analyze and interpret data to find outliers, understand root cause, business impact, correlations/discrepancies, and propose changes/alternate solutions. Discover patterns/root causes, and generate insights to drive product enhancements
Machine Learning: Develop, implement, and optimize machine learning models to support various business initiatives. Analyze and evaluate the quality of data used for model training and testing. Create and present proposals and results in an intuitive, data-backed manner, along with actionable insights and recommendations to drive business decisions
Project Leadership: Lead data science projects from conception to deployment, ensuring timely delivery and high-quality outcomes.
Collaboration: Work closely with product managers, engineers, and other stakeholders to understand business needs and align data solutions accordingly.
Mentorship: Mentor and guide junior data scientists and analysts, fostering a collaborative and growth-oriented environment.
Data Infrastructure: Contribute to the development and maintenance of data pipelines, ensuring data accuracy and accessibility.
Reporting: Prepare and present analytical reports and visualizations to communicate findings to both technical and non-technical audiences.