Senior Data Scientist - Machine Learning

As a Senior Data Scientist with 6-9 years of experience, your role will involve leading complex data science projects and driving business value through statistical modeling, machine learning, and rigorous experimentation. Your responsibilities will include: - Partnering with product managers and business stakeholders to frame complex business problems as quantitative challenges and define measurable project goals. - Designing, developing, and implementing advanced statistical models, machine learning algorithms, and predictive analytics solutions across various domains. - Leading the design and analysis of A/B tests and other experimental methods to validate the impact and effectiveness of models and product features. - Working with large, complex datasets, performing data cleaning, feature engineering, and data analysis using SQL and Python. - Collaborating closely with MLOps and Engineering teams to ensure models are correctly deployed, monitored, and maintained in a production environment. - Providing technical guidance, performing code reviews, and mentoring junior and mid-level data scientists. - Clearly communicating complex findings, methodologies, and business implications to both technical and non-technical audiences. Qualifications required for this role include: - Minimum 6 years of professional experience in a Data Scientist or similar quantitative role. - Expert proficiency in Python and its data science ecosystem (Pandas, NumPy, Scikit-learn, Statsmodels) and strong expertise in SQL for data extraction and manipulation. - Deep, practical knowledge of a wide range of ML techniques and experience with modern deep learning frameworks. - Solid foundation in statistical inference, hypothesis testing, experimental design, and casual inference methods. - Experience working with cloud platforms and big data technologies. - Excellent ability to distill complex analytical results into clear, concise, and actionable business insights. Preferred (Bonus) Skills: - Experience with MLOps principles, tools, and the full model lifecycle deployment. - Advanced knowledge of Natural Language Processing or Computer Vision techniques. - Familiarity with data visualization tools. - Experience in a production-focused or high-throughput environment.,