Job Summary

Lead the development and implementation of machine learning models and data science solutions. They work closely with business stakeholders to identify opportunities for data-driven insights, and they are responsible for developing and implementing cutting-edge algorithms, models, and analytical solutions.

Job Qualifications

• Background education in Data Science, Statistics, Computer Science, Actuarial Science, or related field.

• 5-7+ years of experience in data science, with at least 2 years in a leadership role.

• Prior experience in insurance or financial services is highly preferred.

• Proficiency in Python, R, SQL, and machine learning frameworks (e.g., Scikit-learn, TensorFlow).

• Strong understanding of statistical modeling, predictive analytics, and data visualization tools (e.g., Power BI, Tableau).

• Experience with big data platforms (e.g., Hadoop, Spark) and cloud environments (AWS, Azure, GCP)

• Excellent communication and storytelling skills to translate complex data into actionable insights.

Key Accountabilities

• Collaborate with cross-functional teams to identify business requirements, design experiments, collect and analyze data, and build predictive models. Have expertise that will contribute to enhancing data-driven decision-making processes and improving business outcomes.
• Cleaning, processing, and analyzing data to prepare it for use in machine learning models.
• Developing, testing, and validating machine learning models using appropriate toolsDeploying models into production systems and monitoring their performance.
• Collaborating with the Data Science & Data Engineering team in developing machine learning models and data science solutions.
• Collaborating with business stakeholders to identify opportunities for data-driven insights.
• Defining project objectives and timelines, and managing project execution.
• Contributing to the development of new data science and machine learning solutions.
• Communicating results and insights to stakeholders through presentations and reports.
• Staying up-to-date with industry trends and emerging technologies in data science and machine learning.

Performance Management Accountabilities

1. Demonstrate commitment to corporate values.
2. Take accountability for participating in the performance management cycle.
3. Take action to improve performance on the job.
4. Assist and support co-workers.
5. Take action to manage own personal development.