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Principal Data Scientist page is loaded## Principal Data Scientistlocations: McKinney, Texastime type: Full timeposted on: Posted Todayjob requisition id: REQ44981The Principal Data Scientist is a senior expert and thought leader responsible for driving the design, development, and deployment of scalable AI, ML, and data science solutions to support complex business systems. This role champions technical excellence in MLOps, provides strategic leadership in AI/ML architecture, and partners closely with both technical and business stakeholders to influence outcomes across the enterprise. The ideal candidate combines deep technical expertise with strong executive communication skills and a proven record of delivering measurable business impact.Key Responsibilities:* Drive the design and development of advanced AI/DS/ML solutions that support large-scale and complex business operations* Lead technical excellence in MLOps practices, ensuring scalable and reliable machine learning deployments* Serve as a thought leader in AI/ML, mentoring junior data scientists and guiding architectural decisions and long-term roadmaps* Own cross-functional collaboration efforts with engineering, data engineering, platform, and product teams to ensure seamless delivery and adoption of AI/ML products* Partner with business and functional leaders to align AI/DS strategy with organizational goals and influence decision-making through data-driven insights* Represent the data science function in strategic discussions and executive forumsDirect Manager/Direct Reports:* Direct manager: Sr. Director of Applied AI* Direct reports: No direct reports but may serve as a team lead or player-coach, providing mentorship to senior data scientists and technical contributorsTravel Requirements:* Occasional travel (<10%) may be required for strategic planning, leadership meetings, or cross-functional alignmentPhysical Requirements:* Standard office-based physical requirements including prolonged computer usage and participation in virtual and in-person meetingsWorking Conditions:* Hybrid work environment with a combination of remote and on-site work at 7440 S. Hwy 121, McKinney, TX 75070Minimum Qualifications:* Master’s degree or PhD in a quantitative field (e.g., Statistics, Machine Learning, Applied Mathematics), or at least 10 years of professional experience with a strong track record of building and deploying scalable AI/ML systems* Mastery of a wide variety of machine learning models and AI techniques, including MLOps best practices* Demonstrated ability to translate complex AI/ML concepts into strategic business impact* Experience leading cross-functional initiatives and delivering data products at enterprise scale* Strong communication and influence at the executive levelPreferred Qualifications:* Proven leadership in DS/AI/ML/Optimization within complex enterprise ecosystems* Experience managing high-performing data science teams and leading large-scale, cross-functional programs* Familiarity with regulatory and ethical implications of AI/ML in production environments* Demonstrated success in aligning AI initiatives with key business KPIs and influencing strategic prioritiesMinimum Education:* Master’s degree in a relevant quantitative field or equivalent practical experiencePreferred Education:* PhD in Machine Learning, Statistics, Applied Mathematics, or a related disciplineMinimum Years of Work Experience:* 10+ years of experience in data science, machine learning, or AI, with increasing technical leadership responsibilitiesCertifications:* Not required, but relevant certifications (e.g., Microsoft AI-900 certification, ) are a plus.Competencies:* Deep expertise in AI/ML modeling and MLOps* Strategic and technical leadership across enterprise-level initiatives* Excellent problem-solving and critical thinking abilities* Strong cross-functional collaboration skills* Outstanding executive communication and stakeholder management* High level of initiative, innovation, and long-term strategic thinkingWork Location:* Hybrid – 7440 S. Hwy 121, McKinney, TX 75070 Job Location: SRS Distribution - McKinney7440 State Highway 121 McKinney, TX 75070-3104Equal Opportunity Employer.Veteran Friendly Employer. SRS Distribution believes in hiring military veterans at any level for any position. We know your service trained you in many of the areas we value, such as; leadership, teamwork, performance, integrity, and safety. If your experience matches our requirements, we want you to apply today.### Benefits* Competitive salaries for all team members paid weekly* 401(k) Retirement Plan with company matching* Paid Vacation, Sick Time, Holidays, Birthday, and Floating Holidays* Paid Parental Leave* Medical, Dental and Vision Benefits* Flexible and Dependent Care Spending Accounts* Company paid Life insurance and Short-Term Disability* Additional Life Insurance and Long-Term Disability also offered* Safety Program with Bonuses for our Drivers* Employee Referral Bonus Program*As an Equal Employment Opportunity (EEO) employer SRS Distribution Inc., including all its subsidiaries, provides job opportunities to qualified individuals without regard to actual or perceived race, color, creed, religion, national origin, sex, gender, age, disability, gender identity, sexual orientation, citizenship status, uniform service, veteran status, marital status, genetic information, physical or mental disability, or any other characteristic in accordance with applicable federal, state, and local EEO laws. If you are an individual with a disability or a disabled veteran and require a reasonable accommodation in applying for any posted position, please contact Human Resources at US: 855.556.3221, or by email to: HR@Help.SRSDistribution.com with the nature of your accommodation request and include the Business name, location and title of the job opening. Please allow one (1) business day for a reply.**All candidates for employment must successfully complete pre-employment drug testing (where applicable) and background verification before employment is finalized.* #J-18808-Ljbffr SRS Distribution Inc.

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