Mid Level Senior Data Science Engineer Jobs
Mid level senior data science engineer jobs go to engineers ready to own projects end to end, guide junior teammates, and deliver modeling decisions with limited oversight. Openings are concentrated across Consulting & Professional Services, Technology & Software, and Fintech, with 46% remote or hybrid availability, and employers like Capital One, ZS, and Biogen hiring at this level now.
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Domicile Information: This is a hybrid position located in Memphis, TN; Plano, TX; or Pittsburgh, PA. Candidates must live within 50 miles of the campus location. Employees will be required to work at the FedEx campus location several times per week.
Business Summary
The Manager of Data Science and Operations Research (DSOR) is a crucial leadership position responsible for enabling value creation through the application of Data Science and Operations Research methodologies. This support role requires a blend of strategic business thinking, product design, project management, user empathy, ability to structure the work with stakeholders, and proactive communication of progress or challenges. The Manager will closely partner and support a team of DSOR practitioners, fostering a culture of curiosity, rapid experimentation, and continuous improvement. This position is pivotal in translating business ideas into actionable solutions, ensuring progress through strategic enablement support, design excellence, and championing the use of the scientific method across the enterprise.
Essential Functions
- Builds and promotes advanced analytics and data science capabilities, to extract insights from vast data resources and inform decision-making across the business.
- Hiring, coaching, performance management, and strategic planning, as well as staying current with the latest developments in data science and collaborating with IT and business leadership on advanced analytics initiatives.
- Advanced Quantitative Background: Master’s or Ph.D. in Computer Science, Operations Research, Data Science, Machine Learning, Artificial Intelligence, Statistics, or another quantitative and computational field.
- Strategic AI & ML Leadership: Proven track record of leading and scaling high-performing data science teams to design and deliver advanced machine learning, predictive analytics, and generative AI solutions that drive measurable business value.
- ML Strategy & Experimental Oversight: Direct experience defining the strategic machine learning approach for complex business problems. Proven ability to guide teams through robust feature engineering, experimental design, and model development, while establishing metrics to rigorously measure and ensure model performance, accuracy, and business impact.
- Scientific Method & Experimental Rigor: Strong mastery of the scientific method; proficiency and comfort guiding teams through rigorous hypothesis definition, rapid prototyping, and structured experimentation.
- Opportunity Mobilization: Proven ability to mobilize cross-functional teams to explore new opportunities, foster innovation, and drive continuous experimentation.
- User-Centric Product Vision: Ability to envision and actively contribute to the design of user-centric products that seamlessly integrate data science to drive tangible business value and user engagement.
- Pragmatic & Business-Aligned Decision Making: Strong business acumen with an "engineering" mentality—capable of making pragmatic, rapid trade-offs between model complexity, development velocity, and business impact.
- Modern Software Best Practices: Experience championing high standards for code quality, collaborative workflows (Git-based version control), and reusable, production-ready data science repositories.
- Deep Analytical Empathy: Solid theoretical grounding in the mathematical and statistical core of data science to effectively evaluate team methodologies.
- Ability to critically review and guide the application of advanced modeling, deep learning, or neural network architectures.
- MLOps & Lifecycle Advocacy: Strong conceptual understanding of DevOps, MLOps, and LLMOps practices (CI/CD, model monitoring, and automated pipelines) to support the seamless transition of your team's models into production environments.
- Oversee the organization and strategic management of a portfolio of diverse DSOR projects. Ensure clear milestones are defined, progress is tracked systematically, and high-quality deliverables are met on time.
- Govern incoming business demand, set clear priorities, manage team capacity, and establish strict accountability for project progress.
MINIMUM EDUCATION
Bachelor’s degree or equivalent in Data Science, Operations Research, Engineering, Business Administration, Business Analytics, or related discipline. [Advanced degree preferred.]
MINIMUM EXPERIENCE
Five to eight (5 - 8) years of experience in working with teams in analytics, data science, operations research, engineering or a related field, with demonstrated leadership in managing teams and executing complex projects. Practitioner or practitioner-adjacent experience in analytics, data science, operations research, or related fields inclusive of prototyping and rapid experimentation.
Preferred Qualifications:
Pay Transparency:
Pay: Plano and Pittsburgh: $10,231.53/mo to $13,812.57/mo; Memphis: $9,719.96/mo to $13,121.94/mo
Additional Details: Application Criteria: Upload current copy of Resume (Microsoft Word or PDF format only) and answer job screening questionnaire.
For details on our comprehensive benefits, click here.
Federal Express Corporation is an Equal Opportunity Employer including, Vets/Disability.
Reasonable accommodations are available for qualified individuals with disabilities throughout the application process. Applicants who require reasonable accommodations in the application or hiring process should contact recruitmentsupport@fedex.com.
Applicants have rights under Federal Employment Laws:
- Know Your Rights
- Pay Transparency
- Family and Medical Leave Act (FMLA)
- Employee Polygraph Protection Act
E-Verify Program Participant: Federal Express Corporation participates in the Department of Homeland Security U.S. Citizenship and Immigration Services’ E-Verify program (For U.S. applicants and employees only). Please click below to learn more about the E-Verify program:
- E-Verify Notice (bilingual)
- Right to Work Notice (English) / (Spanish)
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Who's Hiring



Top Industries Hiring
- Consulting & Professional Services
- Technology & Software
- Fintech
- Distribution & Wholesale
- Banking & Financial Services
Mid Level Senior Data Science Engineer Jobs: Frequently Asked Questions
How do I get a mid level senior data science engineer job?
Position yourself around ownership, not just contribution. Highlight projects where you defined the problem, built the solution, and measured the outcome independently. Emphasize production deployments, cross-functional collaboration, and any mentoring you have done. Tailor your resume to show depth in a specific domain, such as NLP, computer vision, or ML infrastructure, rather than breadth alone.
Which companies hire mid level senior data science engineers?
Companies hiring mid level senior data science engineers right now include Capital One, ZS, and Biogen, based on current listings on Migrate Mate as of November 2026. Hiring at this level comes from a mix of established tech firms, healthcare organizations, financial services companies, and retail enterprises that need engineers who can lead modeling work without full senior oversight.
Are there remote mid level senior data science engineer jobs?
Yes, and the share is substantial. About 46% of mid level senior data science engineer openings are remote or hybrid as of November 2026, reflecting strong demand for distributed data science talent across the country. Many teams now hire engineers who can collaborate asynchronously and deliver independently without being on site.
How do I move up to a mid level senior data science engineer role?
The transition from entry level to mid level happens by building a record of full-cycle ownership. Early in your career, focus on shipping end-to-end projects, deepening expertise in one or two technical areas, and taking on work that has measurable business impact. Engineers who can communicate findings to non-technical stakeholders and mentor newer colleagues consistently make the jump faster.
Which industries hire the most mid level senior data science engineers?
Mid Level senior data science engineer roles concentrate in Consulting & Professional Services, Technology & Software, and Fintech, based on current listings on Migrate Mate as of November 2026. These sectors generate large volumes of structured and unstructured data that require engineers with enough experience to own modeling pipelines and translate analytical output into product or operational decisions.