Senior Data Scientist

Elanco•Indianapolis, IN

15d•Hybrid

About The Position

At Elanco (NYSE: ELAN) – it all starts with animals! As a global leader in animal health, we are dedicated to innovation and delivering products and services to prevent and treat disease in farm animals and pets. At Elanco, we are driven by our vision of Food and Companionship Enriching Life and our purpose – all to Go Beyond for Animals, Customers, Society and Our People. At Elanco, we pride ourselves on fostering a diverse and inclusive work environment. We believe that diversity is the driving force behind innovation, creativity, and overall business success. Here, you’ll be part of a company that values and champions new ways of thinking, work with dynamic individuals, and acquire new skills and experiences that will propel your career to new heights. Making animals’ lives better makes life better – join our team today! Your Role: As a Senior Data Scientist at Elanco, you will be a key driver of innovation and efficiency, using advanced analytics to solve complex challenges across our entire value chain. Reporting to the Enterprise Data Science team, you will partner with stakeholders in Strategy & Innovation, R&D, Manufacturing, Finance, and Commercial, to uncover insights, build predictive models, and create data-driven solutions that directly impact animal health and business outcomes. This role is for a curious and creative problem-solver who is passionate about turning data into tangible value. This includes four strategic priorities: Pipeline Acceleration: Optimize the search and approval of high impact medicines with a focus on speed, cost and precision. Manufacturing Excellence: Improve the efficiency, quality and consistency of core manufacturing processes, specifically execution and equipment effectiveness. Sales Effectiveness: Simplify the process to find, trust and consume relevant customer insights that drive sales growth and improved engagement. Productivity: Expand operating margin through efficiency by systematically reducing our operating expenses across the company, improving profitability.

Requirements

Education: A Master’s or PhD in a quantitative field such as Data Science, Statistics, Computer Science, Operations Research, or a related discipline.
Required Experience: 12+ years experience in Data Science or relevant work.
Programming Proficiency: Strong programming skills in Python or R, with expertise in data manipulation and machine learning libraries.
Statistical Rigor: A deep understanding of statistical principles and experimental design, including hypothesis testing, regression, and classification.
Machine Learning Experience: Proven experience applying a range of machine learning techniques (e.g., gradient boosting, clustering, NLP, time-series forecasting) to real-world problems.
Data Visualization and Communication: Expertise in using visualization tools to create compelling stories and the ability to explain complex topics to a non-technical audience.

Nice To Haves

Finance Domain Experience: A strong understanding of financial data, forecasting, budgeting, profitability, and business performance analytics.
Business Acumen: A strong ability to grasp business challenges quickly and a passion for connecting data-driven insights to strategic goals. Experience in animal health, pharma, manufacturing, or commercial analytics is a major plus.
Industry Experience: A deep understanding of life science, covering the business model, regulatory/compliance requirements, risks and rewards. An ability to identify and execute against opportunities within data science that directly support life science outcomes.
Database Skills: Proficiency in SQL for querying and extracting data from relational databases.
Cloud Environment Familiarity: Experience working with Public Cloud, specifically Microsoft Azure and Google Cloud Platform (GCP) and their associated data and analytics services is highly desirable.

Responsibilities

End-to-End Model Development: Partner to design, develop, and validate statistical and machine learning models to address key business questions, from initial data exploration to final analysis.
Analytical Thought Leader: Lead by example and inspire others, analyzing large, complex datasets to extract meaningful insights and solve business problems. This includes elements of Operations Research, using data to optimize decisions and processes
Cross-Functional Problem Solving: Collaborate directly with business units to translate their challenges into data science frameworks. This could include: Strategy & Innovation: Performing market and competitor data analysis. R&D: Accelerating drug discovery, target identification, clinical trial analysis, and drug repurposing. Manufacturing: Optimizing supply chain logistics and improving production yields through predictive quality control and maintenance. Finance: Analyzing financial data, margin, and profitability performance. Commercial: Enhancing sales forecasting, pricing and promotions optimization, personalizing marketing campaigns, understanding customer behaviour, and surfacing data insights via large language models.
Generate Actionable Insights: Go beyond model building to interpret results, synthesize findings, and communicate actionable recommendations to stakeholders at all levels.
Data Storytelling: Use data visualization and clear communication to present complex analytical findings in a compelling and understandable narrative.
Collaborate on Deployment: Partner with Data, AI and ML Engineers to ensure that your models are successfully integrated into business processes and applications.
Drive Innovation: Continuously research and apply new methodologies in machine learning, statistics, and AI to keep Elanco at the forefront of data science.
Champion a Data-Driven Culture: Promote a data-driven culture within Elanco by educating and mentoring colleagues on data science principles and best practices.