Position Summary
CareerVest is seeking a Data Scientist to drive measurable business impact through advanced analytics, machine learning, and rigorous experimentation. In this role, you will partner with leaders across product, engineering, and operations to frame ambiguous business questions as solvable analytical problems and deliver solutions that influence strategy and revenue. This is a high-visibility position suited to someone who is equally comfortable building production-grade models and explaining their value to a non-technical executive audience.

What You'll Do
Translate business objectives into well-defined analytical problems, and design the appropriate modeling or experimental approach to solve them.
Develop, validate, and deploy machine learning and statistical models that improve key business outcomes, from forecasting to segmentation to recommendation.
Design, execute, and analyze controlled experiments (A/B and multivariate testing), and clearly communicate statistically sound conclusions and recommendations.
Build and maintain reproducible data pipelines and analysis workflows in partnership with data engineering.
Create clear visualizations, dashboards, and written narratives that make complex findings accessible to stakeholders at all levels.
Monitor deployed models for drift and performance degradation, and iterate to maintain accuracy and reliability over time.
Establish and uphold best practices in code quality, documentation, peer review, and reproducibility.
Mentor junior analysts and contribute to a culture of analytical rigor and intellectual honesty.

What You'll Bring (Required)
Master's degree in Computer Science, Mathematics, Economics, Engineering, or a related quantitative discipline or equivalent practical experience.
0-2 years of professional experience applying data science methods to real business problems.
Demonstrated proficiency in Python (or R) and SQL, with hands-on experience manipulating large, complex datasets.
Strong working knowledge of supervised and unsupervised learning, statistical inference, and experimental design.
Proven ability to communicate technical results to non-technical stakeholders and influence decisions.
A portfolio of delivered work showing measurable business impact, not just model accuracy.

Nice to Have (Preferred)
Experience deploying models to production and familiarity with MLOps practices.
Hands-on work with cloud platforms (AWS, GCP, or Azure) and distributed computing tools (Spark).
Familiarity with ML frameworks such as scikit-learn, TensorFlow, or PyTorch.
Experience with version control (Git) and collaborative engineering workflows.

Data Scientist

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