JOB DESCRIPTION

The Senior Manager, Finance Reporting & Analysis COE is critical to financial data strategy and execution, and champions data-driven decision making across the enterprise
Reporting to the Senior Director, Finance Reporting & Analysis COE, this role is a key enabler of robust data driven decisions that lead to better financial outcomes for the business. The individual will develop reporting packages and advanced analytics models (e.g., predictive, prescriptive, machine learning, simulation, statistical, AI and generative AI) to solve complex financial problems. You will play an important role in the entire AI/ML lifecycle, from preparing data to developing, deploying, monitoring, and evaluating models. You will also ensure that algorithms are transparent and free from bias.
In this role, you will partner with finance and business users to understand requirements and identify opportunities for data driven initiatives. You will also train citizen data scientists to handle basic data tasks like initial data discovery and cleaning, data pipeline management, process design, visualization and model testing. To be successful in this role, you should possess strong storytelling skills to effectively communicate data insights to key stakeholders. Further, an aptitude and curiosity for understanding business and financial concepts is highly preferred.

BASIC REQUIREMENTS:

• Bachelor’s degree in business, Finance or Accounting.
• MBA or Professional Qualification preferred, CPA a plus.
• Typically requires a number of years of relevant experience in an analytics role demonstrating strong technical and analytical skills and a track record of success working in a team-based environment or the equivalent combination of education and experience. Must have a deep understanding of financial systems, expertise in analytics and presentation/visualization of information and a strategic vision of future ways of working for finance professionals.
• Experience in life science industry preferred, experience in biotech or pharma industries a plus

PREFERRED KNOWLEDGE/SKILLS:

• Experience in leading cross-functional teams and processes; able to manage through ambiguity and influence with and without authority, and work effectively with senior leaders
• Strong interpersonal, written, and verbal communication skills including visualization capabilities and an ability to “tell a story”.
• Exceptional analytical, quantitative, and financial modeling skills
• Possesses a continuous improvement mindset, with flexibility and ability to adapt to change.
• Intellectually curious with an ability to develop innovative reporting and/or analytics to drive business outcomes.
• Forward thinking and change-oriented with an ability to understand how to pace change for effectively adoption.
• Strong formative understanding of statistical and mathematical concepts, theories and applications such as linear algebra, probability theory, calculus, algorithms and data structures
• Strong understanding of algorithms such as linear regression, logistic regression, regularization, decision trees, clustering algorithms, linear GAM (Generalized Additive Models), Principal Component Analysis (PCA) and matrix factorization techniques
• Proficiency in data management, data mining, data analytics, data reporting and quantitative analysis
• Programming: Proficiency in programming languages (e.g., Python).
• Data manipulation: Experience with data manipulation libraries and data visualization tools (e.g., Power BI, Microsoft Co-Pilot)

• Develops enterprise reporting capabilities, to drive efficiency and add value for the global finance teams and, ultimately, to enable self-service reporting of critical business KPIs by empowered senior leaders.
• Supports the transformation of the finance function through analytics, by Identifying data opportunities to solve business challenges. Collaborates with Functional leadership to identify and prioritize key financial analytics initiatives. Define and scope the finance use cases by:
• Developing and implementing advanced analytics models:
• Understand business use cases and determine the appropriate algorithms for a given situation.
• Develop, test, and validate various AI/ML models, then deploy the AI/ML models into production and integrate them with existing data and IT systems.
• Minimize bias in algorithms using testing methods like cross-validation, A/B testing, bias assessments and stress-testing.
• Effectively communicate insights to finance stakeholders:
• Build reports, dashboards, and visualizations to democratize data insights.
• Use data storytelling to deliver business insights to improve stakeholder understanding and uptake.
• Clearly communicate underlying methodologies and decision-making processes of models and algorithms providing more transparency for stakeholders.
• Collaborate with various departments to maximize the value of AI, ML and other data science initiatives:
• Partner with data engineers to build and enhance data pipelines.
• Collaborate with domain experts across the organization to leverage the existing data and AI assets and create value for the finance function. Foster collaboration with other data science teams within the organization and encourage the reuse of artifacts.
• Continuously evolve processes and tools related to data analysis, visualization and storytelling to improve decision making.
• Maintain governance and compliance, ensure that data and algorithms driving key business decisions are transparent and free of bias. Apply quality control, data validation and cleansing processes to new and existing data sources, working alongside DTE organization to ensure adherence to Enterprise requirements.