1

Data Science Phd Jobs in Kentucky (NOW HIRING)

Master's or PhD degree in Computer Science, Machine Learning, Engineering, or another highly ... Experience working with data pipelines, APIs, ETL workflows, and cloud-based data platforms

Master's or PhD degree in Computer Science, Machine Learning, Engineering, or another highly ... Experience working with data pipelines, APIs, ETL workflows, and cloud-based data platforms

next page

Showing results 1-20

Data Science Phd information

What can you do with a doctorate in data science?

A doctorate in data science prepares individuals for advanced roles such as data scientist, research scientist, or machine learning engineer, often involving complex data analysis, modeling, and algorithm development. It enables expertise in programming languages like Python or R, statistical methods, and data management tools, opening opportunities in academia, industry, and research institutions.

What are the key skills and qualifications needed to thrive as a Data Science PhD, and why are they important?

To thrive as a Data Science PhD, you need advanced expertise in statistics, machine learning, data analysis, and a doctoral degree in a quantitative field. Proficiency in programming languages like Python or R, experience with big data frameworks (e.g., Spark, Hadoop), and familiarity with data visualization tools are typically required. Critical thinking, problem-solving, and strong communication skills help you translate complex data insights for diverse stakeholders. These skills are vital for driving innovative research, making data-driven decisions, and contributing impactful solutions in data-centric environments.

Is PhD worth it for data science?

A PhD in data science can enhance expertise in advanced analytics, research, and specialized skills, which may lead to higher-level roles and increased salary potential. However, it also requires significant time and financial investment, and many data science positions value practical experience and skills in programming, machine learning, and data manipulation over formal degrees.

What is the salary of a PhD in data scientist?

A Data Science PhD typically earns between $100,000 and $150,000 annually, depending on experience, industry, and location. Advanced degrees and expertise in machine learning, statistical analysis, and programming tools like Python or R can lead to higher compensation, especially in tech and research sectors.

What are some common challenges faced by Data Science PhDs when transitioning from academia to industry roles?

Data Science PhDs often encounter challenges such as adapting to the faster pace and collaborative nature of industry projects compared to academic research. In industry, there is a greater emphasis on delivering practical solutions within tight deadlines and working closely with cross-functional teams like engineering and product management. Additionally, data science work in industry may require balancing technical rigor with business impact, often prioritizing actionable insights over exhaustive analysis. Building strong communication and stakeholder management skills can help ease this transition.

Is 40 too late for data science?

Data science PhDs can pursue careers at any age, including at 40 or older. Success depends on skills, experience, and continuous learning in areas like programming, statistics, and machine learning, rather than age alone.

What is a Data Science PhD?

A Data Science PhD is a doctoral-level degree focused on advanced research in data science, which combines elements of statistics, computer science, and domain expertise. Students in a Data Science PhD program typically work on developing new methods for analyzing large datasets, creating machine learning algorithms, and addressing complex problems in areas such as artificial intelligence, data mining, and predictive analytics. Graduates are prepared for careers in academia, research, and industry, where they can lead data-driven projects and contribute to advancements in the field.
What are popular job titles related to Data Science Phd jobs in Kentucky? For Data Science Phd jobs in Kentucky, the most frequently searched job titles are:
What cities in Kentucky are hiring for Data Science Phd jobs? Cities in Kentucky with the most Data Science Phd job openings:
Infographic showing various Data Science Phd job openings in Kentucky as of July 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, 1% Temporary, and 3% Contract. Highlights an 90% Physical, 2% Hybrid, and 8% Remote job distribution.

Principal Supply Chain Data Scientist

Genentech

Louisville, KY • On-site

Full-time

Re-posted 4 hours ago


Genentech rating

8.8

Company rating: 8.8 out of 10

Based on 22 frontline employees who took The Breakroom Quiz

11th of 86 rated pharmaceutical


Job description

Job Summary:
Genentech, a pioneering biotechnology company now part of the Roche Group, is dedicated to developing groundbreaking medicines for serious health challenges. The Principal Supply Chain Data Scientist will lead advanced analytical initiatives to optimize pharmaceutical distribution processes, leveraging data science methodologies and technologies to enhance supply chain strategy and decision-making.
Responsibilities:
• Design, implement, and validate advanced algorithms and predictive models to solve supply chain challenges in a high-pace distribution operation.
• Apply machine learning, statistical methods, and optimization techniques to enhance forecasting, inventory management, and logistics planning.
• Analyze large datasets and business systems to identify trends, patterns, and innovative opportunities using data mining tools and statistical approaches.
• Develop advanced AI-driven tools for improved planning accuracy, distribution efficiency, and warehouse operations.
• Develop and maintain reports and dashboards using Tableau.
• Generate and distribute reports using SQL Reporting Services.
• Develop and implement distribution plans to optimize product movement from manufacturing facilities to customers.
• Create and execute detailed schedules for deliveries, warehouse operations, and related activities.
• Effectively manage warehouse capacity modeling and assessments to ensure accurate and timely insights.
• Conduct space utilization analysis, evaluating how effectively warehouse space is being used and identifying areas for improvement.
• Plan inbound/outbound logistics to meet daily and weekly distribution schedules.
• Plan efficient delivery routes and schedules for cost-effective transportation of medications.
• Analyze the distribution network to identify bottlenecks, optimize routes, and improve overall efficiency and cost-effectiveness.
• Drive the creation of digital twin models for scenario planning and strategic decision-making, ensuring efficient and sustainable supply chain practices.
• Evaluate and propose innovation opportunities through investment modeling and process improvements.
• Serve as a strategic advisor, translating business challenges into actionable, data-driven insights.
• Present findings, recommendations, and strategic plans to senior leadership, aligning advanced analytics with organizational goals.
• Act as the subject matter expert in supply chain analytics, providing guidance across initiatives and projects.
• Manage data governance frameworks, ensuring integrity, accuracy, and compliance with regulations.
• Track key performance indicators (KPIs) such as on-time delivery rates, inventory turns, and order fulfillment rates to identify areas for improvement.
• Analyze performance data to identify issues and implement changes to enhance efficiency and customer service.
• Address disruptions in the distribution process promptly and develop contingency plans for potential risks.
• Regularly review performance metrics and adjust processes for ongoing optimization.
• Use advanced planning systems and AI-driven tools to improve planning accuracy and optimize warehouse operations.
• Create comprehensive documentation and reports to communicate insights and recommendations.
• Ensure compliance with relevant regulations and guidelines related to pharmaceutical distribution and supply chain management.
• Create, implement, and manage data integration workflows using SQL Integration Services.
• Develop multidimensional cubes and tabular models in SQL Analysis Services.
• Ensure data integrity, accuracy, and security across all BI systems.
• Continuously improve BI processes and methodologies to enhance efficiency and effectiveness.
Qualifications:
Required:
• Bachelor's degree in a quantitative or technical field (e.g., Data Science, Supply Chain Management, Logistics, Operations Research, Mathematics, or Computer Science) required.
• Over 10 years of experience in supply chain analytics, business intelligence, or advanced data science roles, with a minimum of 8 years in advanced supply chain analytics.
• Proficiency in Python, SQL, and cloud-based tools with experience in developing machine learning models and optimization algorithms.
• Hands-on experience with advanced statistical and machine learning packages (e.g., scikit-learn, TensorFlow, R).
• Skilled in optimization tools (e.g., Gurobi, CPLEX) and inventory management platforms.
• Deep understanding of supply chain management principles, including forecasting, inventory optimization, logistics planning, and procurement.
• In-depth experience with pharmaceutical distribution requirements, industry standards, and compliance regulations.
• Familiarity with warehouse operations, capacity planning, network design, and space utilization analysis.
• Familiarity with SQL Integration Services, SQL Analysis Services, and SQL Reporting Services.
Preferred:
• Advanced degrees (Master's, PhD) in the same fields strongly preferred, with demonstrated expertise in supply chain analytics.
• Demonstrated track record of implementing analytics within a supply chain context, with pharmaceutical distribution expertise strongly preferred.
Company:
Genentech is a biotechnology research company that specializes in genetic testing and personalized medicines. Founded in 1976, the company is headquartered in South San Francisco, USA, with a team of 10001+ employees. The company is currently Late Stage.

What Genentech employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


Genentech logo

About Genentech

Sourced by ZipRecruiter

A member of the Roche Group, Genentech has been at the forefront of the biotechnology industry for more than 40 years, using human genetic information to develop novel medicines for serious and life-threatening diseases. Genentech has multiple therapies on the market for cancer & other serious illnesses. Please take this opportunity to learn about Genentech where we believe that our employees are our most important asset & are dedicated to remaining a great place to work.

Industry

Scientific research and development services

Company size

10,000+ Employees

Headquarters location

South San Francisco, CA, US

Year founded

1976

Social media