Senior Data Scientist

Essential Duties and Responsibilities include the following but are not limited to the job specifications contained herein. Additional duties or job functions that can be performed safely may be required as deemed necessary by supervisory personnel.

• Lead the development and deployment of machine learning models and financial analytics tools to optimize business and financial strategies.

• Analyze large datasets from various sources to generate actionable insights related to financial performance, risk management, forecasting, and pricing.

• Design and implement statistical models to identify trends, correlations, and opportunities for business growth and process improvement.

• Collaborate with finance, operations and leadership teams to translate financial and business needs into data science solutions.

• Perform financial forecasting, scenario analysis and risk modeling using advanced data science techniques.

• Present data-driven insights and recommendations to senior leadership, driving strategic business decisions.

• Mentor and guide junior data scientists, ensuring best practices in model development, testing and deployment.

• Stay current with advancements in machine learning, data science and financial technologies to continuously improve processes and models.

• Lead A/B testing, evaluate model performance using appropriate metrics, and refine models as needed.

• Partner with stakeholders across the business to define key financial metrics and develop reports and dashboards for executive-level consumption.

Qualifications:

• Proficiency in programming languages such as Python, R, and SQL for data manipulation and model development.

• Proven experience with financial modeling, risk assessment, and quantitative finance techniques.

• Strong background in machine learning algorithms (e.g., regression, classification, clustering, time series forecasting) and their application in business and finance.

• Excellent communication skills, with the ability to present complex financial data and analysis to non-technical audiences.

• Experience with data visualization tools such as Tableau, Power BI, or Matplotlib.

• Proven track record of applying data science techniques to drive financial performance and solve business problems.

• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and big data tools is a plus.

• Experience working with financial data, such as stock prices, credit scores, or financial forecasting.

• Knowledge of time series forecasting, econometrics, and optimization techniques.

• Expertise in statistical modeling, financial risk analysis, or algorithmic trading.

• Experience with tools like TensorFlow, PyTorch, or scikit-learn for machine learning model development.