Senior Data Scientist

Key Skills

Aartificial intelligence, AWS, Azure, Data science techniques, Databricks, IT, Kubernetes-K8s, Machine learning techniques, Operations, Python Programming, PyTorch, R Programming, Scikit-learn, SQL, TensorFlow

Job Description

AI Governance & Responsible AI Team at GEICO seeks driven, analytically minded, and inquisitive Senior Data Scientist with an excellent data science foundation, expertise in machine learning and artificial intelligence, and a history of developing and demonstrating technical expertise applied to ML projects. The objective of the role is to help bring together Machine Learning lifecycle and accountable and responsible AI (RAI) principles. The work includes shaping and implementing the technical framework and methodology for identifying, testing and mitigating AI risks, through the product development and deployment phases of Machine Learning (ML) models.

You will support critical departmental or cross-functional ML initiatives and projects across various functional areas, including Claims, Customer Experience, Marketing, HR. This is a critical role with high visibility across the organization, and impact on all AI initiatives in GEICO.

As part of the AI Governance Team, you will help with a multi-year transformational journey focusing on:

• Enabling Responsible AI: Working closely with Data Scientists from various functional areas, we help enable transparent, accountable, compliant, and fair ML model development
• Shaping Risk-Based Model Governance: Designing and implementing an AI governance workflow process to address AI-specific risks, including addressing the emerging regulatory framework(s)
• Driving Simplicity and Standardization: Integrating related work to reduce redundancy, focus on the most critical and effective elements of AI risks and control ecosystem
• Promoting Automation: Advancing the journey to reduce manual processes, enhance monitoring techniques and seizing the power of automation

Essential Functions:

Modeling: Develop, validate, and implement predictive models that drive business insights and guide decision-making. Exploratory Data Analysis (EDA): Dive deep into complex datasets to uncover patterns, correlations, and insights. Solution & System Design: Architect scalable data solutions that align with company objectives and stakeholder requirements. Risk Management:

• Assess, propose, and develop toolsets and techniques for detection and mitigation of bias in data and ML models; use these tools in testing and validating models
• Assess, challenge, and at times defend state-of-the-art decision-making systems to internal partners
• Monitors deployed models, test performance against monitoring standards, identify impact of drifts
• Partner cross-functionally with data scientists, business analysts, software engineers, and product managers to identify and mitigate risks inherent in machine learning models

Testing and Validation:

• Test and validate models under development in meeting model governance and responsible AI standards and goals
• Develop new ways to stress test and identify weak region/spots in model predictions early in the product development lifecycle

Research & Development: Drive R&D initiatives utilizing innovative methodologies and techniques that can offer a competitive edge. Project Planning & Management: Ensure timely delivery of projects by coordinating with cross-functional teams, setting milestones, and monitoring progress. Initiate and own multi-dependency projects across teams in order to drive business value. Team Leadership: Mentor and guide junior data scientists, analysts, and engineers to foster a collaborative environment. Cross-Functional Collaboration: Work closely with departments like IT, Product, and operations to ensure alignment and facilitate data-driven strategies. Software Engineering Practices: Employ robust software practices to ensure the scalability, reliability, and performance of data-driven applications. Communication: Present complex findings in an accessible manner to senior managers, officers, and other stakeholders, influencing strategic decisions. Take ownership of critical AI systems and manage external stakeholders. Continuous Learning & Improvement: Stay updated with the latest in the realm of data science, AI, and insurance, fostering a culture of continuous learning.

Key Competencies:

• Creativity & Intellectual Curiosity: Passion for exploring new techniques and out-of-the-box thinking.
• Attention to Detail: Ensure the accuracy and reliability of results.
• Adaptability: Ability to thrive in a dynamic environment, adjusting to evolving business needs.
• Interpersonal Sensitivity: Establish and maintain effective relationships within and outside the team.
• Decision Making: Independently execute sound decisions based on data, logic, and business priorities.
• Strategic Acumen: Ability to foresee industry trends and guide the team towards long-term goals.

Requirements:

• A Master’s Degree in Data Science, Computer Science, Statistics, Mathematics, or a related field and at least 4 years of relevant work experience or a PhD in these fields and at least 2 years of relevant work experience.
• 4+ years’ experience with and solid proficiency in machine learning techniques and predictive modeling
• 4+ years’ experience with and solid proficiency in a programming language such as Python, R, and SQL.
• 3+ years’ experience with machine learning frameworks and tools like TensorFlow, PyTorch, Scikit-learn, etc.
• 2+ years’ experience with Azure, Databricks, realtime Deployment, Kubernetes
• 2+ years’ experience with cloud-based services such as AWS or Microsoft Azure

Benefits:

As a full time associate, you’ll enjoy our Total Rewards Program to help secure your financial future and preserve your health and well-being, including:

• Premier Medical, Dental and Vision Insurance with no waiting period
• Paid Vacation, Sick and Parental Leave
• 401(k) Plan
• Tuition Assistance including Direct Billing and Reimbursement payment plan options
• Paid Training, Licensures, and Certificates