Lead Data Scientist – Finance Analytics, Automation & AI (AAA)

Position Purpose

The Lead Data Scientist is responsible for leading data science initiatives that drive business profitability and increased efficiencies for the Finance and Accounting organization. This role applies a deep understanding of advanced analytics, machine learning, and AI to develop innovative solutions that inform financial decision-making and enable automation. The Lead Data Scientist serves as a key partner in identifying business opportunities and applying cutting-edge data science techniques to gain a competitive advantage.

As a Lead Data Scientist, you will lead large data science projects, from conception to production. You will be responsible for identifying the best technology and approach and providing technical leadership and mentorship to data scientists and analysts on the team. This role is expected to own the development and deployment of reusable algorithms and models, ensuring solutions are robust, well-documented, and scalable within our Google Cloud ecosystem.

Key Responsibilities

• 30% Solution Development – Utilize expertise when designing and developing algorithms and models to use against large datasets to create business insights; Make appropriate selection, utilization and interpretation of advanced analytics methodologies; Effectively communicate insights and recommendations to both technical and non-technical leaders and business customers/partners; Clearly communicate impacts of recommendations to drive alignment and appropriate implementation
• 25% Project Management & Team Support – Lead and manage large and complex projects and teams; Provide direction on prioritization of work and ensure quality of work; Provide mentoring and coaching to more junior roles to support their technical competencies; Collaborate with managers and team in the distribution of workload and resources; Support recruiting and hiring efforts for the team; Serve as a technical subject matter expert (SME) for one or more data science methods, both predictive and prescriptive; Lead data science communities across several business units
• 20% Business Collaboration – Leverage extensive business knowledge into solution approach; Effectively develop trust and collaboration with internal customers and cross-functional teams; Provide technical education on advanced analytics to data science community; Partner with IT to understand potential for new tools and ways to maintain technical agility for data science; Actively seek out new business opportunities to leverage data science as a competitive advantage
• 25% Technical Exploration & Development – Seek further knowledge on key developments within data science by attending conferences and publishing papers; Participate in the continuous improvement of data science and analytics by developing replicable solutions (for example, codified data products, project documentation, process flowcharts) to ensure solutions are leveraged for future projects; Define best practices and develop clear vision for data analysis and model productionalization; Ownership of library of reusable algorithms for future use, ensure developed code/models are documented; Develop mastery in one or more prescriptive modeling techniques, like optimization, computer vision, recommendation, search or NLP

Direct Manager/Direct Reports

• This position typically reports to manager or above
• This position has 0 Direct Reports and leads/manages projects

Travel Requirements

• Typically requires overnight travel less than 10% of the time

Physical Requirements

• Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles

Working Conditions

• Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable

Minimum Qualifications

• Must be eighteen years of age or older
• Must be legally permitted to work in the United States
• Demonstrated expertise in predictive modeling, data mining and data analysis
• Demonstrated expertise utilizing statistical techniques to identify key insights that help solve business problems

Preferred Qualifications

• Extensive Experience: Minimum of 8-10 years of experience in data science, with at least 3-5 years in a leadership or senior role, preferably in a large-scale, complex enterprise
• Cloud Platform Expertise: Extensive experience with Google Cloud Platform (GCP), particularly BigQuery and machine learning services
• Advanced Modeling: Proven experience with a variety of machine learning and statistical modeling techniques, including regression, classification, clustering, time-series forecasting, and optimization
• AI/MLOps Proficiency: Strong understanding of the end-to-end machine learning lifecycle, from data exploration and model development to deployment, monitoring, and maintenance. Experience with tools for MLOps and model orchestration
• Programming and Tools: Mastery of programming languages like Python for data manipulation, statistical analysis, and model building
• Domain Knowledge: Experience with finance or accounting data, and an understanding of key financial processes and systems
• Leadership and Mentorship: Demonstrated ability to lead complex projects, mentor junior team members, and drive a culture of continuous learning and technical excellence

Minimum Education

• The knowledge, skills and abilities typically acquired through the completion of a bachelor's degree program or equivalent degree in a field of study related to the job

Preferred Education

• No additional education

Minimum Years of Work Experience

• 10

Preferred Years of Work Experience

• No additional years of experience

Competencies

• Attracts Top Talent: Attracting and selecting the best talent to meet current and future business needs
• Builds Networks: Effectively building formal and informal relationship networks inside and outside the organization
• Business Insight: Applying knowledge of the business and the marketplace to advance the organization's goals
• Collaborates: Building partnerships and working collaboratively with others to meet shared objectives
• Communicates Effectively: Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences
• Cultivates Innovation: Creating new and better ways for the organization to be successful
• Develops Talent: Developing people to meet both their career goals and the organization's goals
• Instills Trust: Gaining the confidence and trust of others through honesty, integrity, and authenticity
• Optimizes Work Processes: Knowing the most efficient and effective processes to get things done, with a focus on continuous improvement
• Persuades: Using compelling arguments to gain the support and commitment of others
• Self-Development: Actively seeking new ways to grow and be challenged using both formal and informal development channels
• Strategic Mindset: Seeing ahead to future possibilities and translating them into breakthrough strategies
• Tech Savvy: Anticipating and adopting innovations in business building digital and technology applications