Senior Data Scientist

Why join OCLC? 

OCLC is consistently recognized as a best place to work by several independent programs. We recognize and reward people and results with a comprehensive Total Rewards package. This means competitive compensation that reflects your unique contributions—performance, experience, and skills—along with exceptional benefits, including best-in-class health coverage, retirement plans with generous company contributions, and a commitment to your overall well-being.

• We know the best ideas don’t always happen at a desk. Take a walking meeting around our 100-acre campus or enjoy lunch on the patio. We’re committed to your success—both personally and professionally. Hybrid work environment: For many roles, three days a week on-site, with occasional additional days based on business needs. 

• Free use of our on-site fitness center, gym sports, group exercise classes, and game room 

• Onsite catering and cafeteria subsidized by OCLC 

• Health and wellness events 

• Work environments with individual and team spaces and the latest technology tools 

• Paid parental leave and adoption assistance 

• Tuition reimbursement and Public Service Loan Forgiveness eligibility 

• Company-subsidized pricing on local tickets and memberships 

Join us in transforming how people everywhere access information and be part of a mission-driven team that makes a global impact. 

The job details are as follows:

As a Senior Data Scientist you will work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions

Responsibilities: 

• Design, develop, and deploy advanced machine learning models and statistical algorithms to solve complex business problems and drive data-driven decision making across the organization
• Conduct rigorous statistical analysis, validate hypotheses, and provide actionable insights to stakeholders
• Build and optimize end-to-end data pipelines, including data extraction, transformation, and feature engineering to support scalable ML solutions
• Review, scale, and enhance operationalized statistical and machine learning models and algorithms, and quantify improvements in terms of business efficiency or customer experience
• Create repeatable processes and scalable data products by, for example, automating feedback loops for production statistical or machine learning models
• Perform exploratory data analysis on large-scale datasets to uncover patterns, trends, and opportunities for innovation
• Implement MLOps best practices, including model versioning and monitoring to ensure reliable model performance in production.
• Mentor junior data scientists and contribute to the development of team capabilities through code reviews, knowledge sharing, and best practice documentation
• Influence functional teams to develop best practices across the organization
• Identify areas of opportunity for data science to effect change and drive strategic business impact
• Maintain engagement with the data science community and current industry developments to assist in driving technical data science team vision and strategy

Requirements: 

• Master’s Degree in a quantitative field (Mathematics, Computer Science, or Statistics or related quantitative fields) and 5+ years professional experience in a data science role or PhD in a quantitative field and 2+ years professional experience in a data science, machine learning, or related analytical role
• Deep understanding of machine learning algorithms, statistical modeling techniques, and their practical applications, along with extensive experience using ML frameworks and libraries such as scikit‑learn, TensorFlow, or similar tools.
• Strong SQL skills and experience working with large-scale data warehousing platforms, particularly Snowflake
• Expert proficiency in a scripting language such as Python, including Python data libraries (numpy, pandas, matplotlib, scikit-learn) and strong programming skills in Java or similar languages
• Intermediate proficiency in a low-level or performant language
• Expert proficiency in working within a cloud computing environment using software development best practices
• Hands-on experience with cloud platforms (AWS and/or Azure) including services for data storage, processing, and model deployment
• Proven track record of deploying machine learning models to production environments and measuring their business impact
• Experience automating production-quality statistical or machine learning models at scale with expert understanding of their underlying mathematical and statistical theory
• Experience with version control (Git), containerization (Docker), and CI/CD practices
• Experience and expertise solving complex and highly impactful quantitative business problems
• Self-starting attitude; ability to spearhead new data science initiatives and collaboration across functional teams
• Demonstrates excellent communication skills with ability to explain statistic and mathematical concepts to non-experts

Working Conditions: Normal office environment.

ADA/EAA: The above statements cover what are generally believed to be principal and essential functions of this job. Specific circumstances may allow or require some people assigned to the job to perform a somewhat different combination of duties.