Data Scientist

About the position

The Data Science Analyst position involves collaborating with team members and business partners to leverage data for impactful decision-making. The role focuses on data extraction, manipulation, and analysis using advanced statistical and machine learning techniques to develop predictive models and actionable insights. The analyst will also create data visualizations and ensure smooth transition of models into production within a cloud environment, while staying updated on industry trends.

Responsibilities

• Work closely with team members and business partners to identify and prioritize key questions and drive impactful data-based decisions.
• Extract, manipulate, and clean data from diverse sources using SQL and Python, preparing it for detailed analysis and model development.
• Develop and implement sophisticated predictive and prescriptive models using statistical and machine learning techniques.
• Create clear, compelling data visualizations to effectively communicate findings to both technical and non-technical stakeholders.
• Effectively transition models from development to production within the organization's existing cloud ecosystem.
• Participate actively in project planning and prioritization sessions to ensure data initiatives align with business goals.
• Stay updated with the latest industry trends and tools and integrate this knowledge to improve methodologies and solutions.

Requirements

• Master's degree or higher in a relevant analytical field.
• Hands-on experience building and optimizing data solutions using Python.
• Experience solving problems using a variety of statistical and machine learning techniques.
• Hands-on experience using statistical or machine learning frameworks to solve real-world problems (e.g., statsmodels, scikit-learn, PyTorch).
• Knowledge of methods like Logistic Regression, Time Series Analysis, GLMs, Mixed Modeling, Multivariate Statistics, Predictive Modeling, Decision Trees, Gradient-Boosted Trees, Random Forests, and Neural Networks.
• Proactive approach to identifying problems and developing innovative solutions.

Nice-to-haves

• Experience with version control systems such as GitHub, and familiarity with CI/CD practices to streamline model deployment and code management.
• Hands-on experience with cloud-based machine learning platforms (e.g., AWS SageMaker or Azure ML).
• Demonstrated ability to lead through influence, effectively navigating and prioritizing complex cross-departmental projects.
• Capability to replace and bridge existing legacy infrastructure and processes.

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• 401K contributions
• Paid time off (PTO)
• Sick leave