Job Description
Join Nexus Analytics at the forefront of data-driven innovation! We're seeking a visionary Senior Data Scientist to transform complex datasets into actionable business intelligence. In this pivotal role, you'll architect cutting-edge machine learning solutions, collaborate with cross-functional teams to solve high-impact challenges, and shape our data strategy for global markets.
Our ideal candidate thrives in fast-paced environments and possesses a blend of technical excellence and business acumen. You'll leverage our state-of-the-art cloud infrastructure to build predictive models, design A/B tests, and present insights to C-level stakeholders. If you're passionate about harnessing data to drive measurable business outcomes, we want to hear from you!
Responsibilities
- Develop and deploy scalable machine learning models for predictive analytics and business optimization
- Collaborate with product teams to define data requirements and translate business problems into analytical solutions
- Design and execute complex A/B tests to validate hypotheses and drive product decisions
- Create compelling data visualizations and dashboards using tools like Tableau and Power BI
- Mentor junior data scientists and establish best practices for data modeling and validation
- Present analytical findings to executive leadership and translate technical insights into strategic recommendations
- Stay current with emerging ML techniques and implement industry-leading algorithms
Qualifications
- Master's or PhD in Statistics, Computer Science, Mathematics, or related quantitative field
- 5+ years of experience in data science with proven track record of deploying production ML models
- Expertise in Python (Pandas, Scikit-learn, TensorFlow) and SQL
- Strong statistical background with experience in experimental design and causal inference
- Proficiency with cloud platforms (AWS/GCP) and big data technologies (Spark, Hadoop)
- Exceptional communication skills with ability to articulate complex technical concepts to non-technical stakeholders
- Portfolio demonstrating impactful data science projects with measurable business results
- Experience with MLOps and CI/CD for ML pipelines