Myriad Genetics
Lead Data Scientist - Remote (Data Scientist IV)
Myriad Genetics, Salt Lake City, Utah, United States, 84193
Lead Data Scientist - Remote (Data Scientist IV)
Job Summary:
As the Lead Data Scientist for the Myriad Data Services Team, you will be responsible for establishing and developing the data science program. You will work cross-functionally with R&D, technology, and business operations teams to implement data-driven solutions that enhance patient care, drive business insights, and improve operational efficiencies. This role requires a strategic and innovative leader with strong technical expertise in data science, artificial intelligence (AI), and machine learning (ML), capable of driving impactful solutions across both scientific and business functions.
Key Responsibilities:
Program Development and Leadership:
Lead the development and implementation of Myriad's data science strategy, with a focus on business operations.
Build and lead a team of data scientists to support key business and research objectives.
Foster collaboration between technology and business operations teams to ensure data science, AI, and ML initiatives align with the broader goals of the organization.
Work closely with the business team to refine requirements and build success criteria
Promote best practices in data science and analytics.
Data Science and Machine Learning Solutions:
Create, maintain, and refine predictive models and machine learning algorithms to analyze large datasets from diverse sources
Develop advanced data tools and pipelines for processing, cleaning, and analyzing complex datasets, both scientific and operational (e.g., customer data, sales, financial metrics).
Utilize AI and ML techniques to drive process automation, enhance decision-making, and deliver insights for improving operational performance across marketing, sales, finance, and supply chain functions.
Infrastructure and Platform Development:
Design and implement scalable, efficient machine learning technology stack to support ongoing machine learning needs for both scientific and business operations.
Lead MLOps initiatives to streamline the model lifecycle, including continuous integration, deployment, and monitoring in production environments.
Ensure reliability and scalability of machine learning platforms through automation, adherence to industry best practices, and cutting-edge AI/ML solutions.
Collaborate with cloud teams to maintain and enhance AWS and DNAnexus platforms for seamless data integration, model deployment, and process optimization.
Business Operations Focus:
Leverage data science, AI, and ML to provide actionable insights that drive improvements in customer experience, sales forecasting, and operational efficiency.
Develop models to support business operations in areas such as demand forecasting, customer segmentation, churn prediction, and resource allocation.
Use advanced analytics to identify business trends, optimize pricing strategies, and enhance marketing campaigns.
Data Governance, Compliance, and Ethical AI:
Ensure compliance with data privacy regulations (e.g., GDPR, HIPAA) and implement strong data governance practices to ensure data security and ethical use of AI/ML models.
Advocate for responsible AI and machine learning practices, ensuring fairness, transparency, and accountability in all models deployed.
Promote best practices in ethical AI development and ensure adherence to industry standards and company policies on data usage.
Collaboration and Communication:
Work closely with molecular biologists, computational scientists, business analysts, and cross-functional teams to translate complex scientific and business problems into data science applications.
Present complex data findings and actionable insights to both technical and non-technical stakeholders, including senior leadership.
Promote knowledge sharing across departments to ensure data-driven decision-making, operational innovation, and business growth.
Collaborate with revenue, commercial, and lab product teams to analyze large datasets to extract actionable insights, identify trends, and make recommendations.
Documentation and Project Management:
Thoroughly document workflows, models, and analytical processes to ensure transparency and reproducibility.
Lead project management efforts, ensuring data science and business operations initiatives are completed on time and meet stakeholder expectations.
Qualifications:
Education:
PhD in Data Science, Bioinformatics, Computer Science, Business Analytics, or a related field with 6+ years of experience; OR
MS degree with 8+ years of experience; OR
BS degree with 10+ years of experience.
Experience:
4+ yrs experience in AWS, 2+ yrs experience in Snowflake. Sagemaker/ DataIKU / ML Flow / Meta Flow experience is a plus.
Proven experience building and deploying machine learning models, with a focus on business operations in AWS stack or snowflake. ML ops experience is required.
Proficiency in Python, and data science libraries (scikit-learn, TensorFlow, PyTorch, etc.).
Experience in Jira, github is a must. Must be familiar with Agile methodologies and be able to participate in scrums, sprint planning, sprint refinements, and retro.
Experience working with large-scale relational databases, cloud platforms (AWS, Google Cloud), and unstructured data.
Skills:
Strong understanding of AI, ML, and statistical methods applied to business data, and scientific data is a plus.
Ability to develop predictive models for customer behavior, sales forecasts, and operational efficiency.
Expertise in data mining, natural language processing (NLP), and optimization techniques.
Demonstrated leadership skills with the ability to mentor and guide junior data scientists and a small team of data scientists.
Excellent communication skills, capable of explaining technical concepts to non-technical stakeholders.
Strong organizational and project management abilities.
Preferred Skills:
Experience with time-series data, A/B testing, and predictive modeling for business functions.
Familiarity with AI and ML tools for automating business processes and improving operational efficiency.
Compensation and Benefits:
Myriad offers a competitive salary and comprehensive benefits package, including health insurance, 401(k), and opportunities for professional development.
EEO
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. In hiring and all other employment decisions, we prohibit discrimination and harassment on the basis of any protected characteristic, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. In accordance with applicable law, we make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as any mental health or physical disability needs.
#LI-Remote
Job Summary:
As the Lead Data Scientist for the Myriad Data Services Team, you will be responsible for establishing and developing the data science program. You will work cross-functionally with R&D, technology, and business operations teams to implement data-driven solutions that enhance patient care, drive business insights, and improve operational efficiencies. This role requires a strategic and innovative leader with strong technical expertise in data science, artificial intelligence (AI), and machine learning (ML), capable of driving impactful solutions across both scientific and business functions.
Key Responsibilities:
Program Development and Leadership:
Lead the development and implementation of Myriad's data science strategy, with a focus on business operations.
Build and lead a team of data scientists to support key business and research objectives.
Foster collaboration between technology and business operations teams to ensure data science, AI, and ML initiatives align with the broader goals of the organization.
Work closely with the business team to refine requirements and build success criteria
Promote best practices in data science and analytics.
Data Science and Machine Learning Solutions:
Create, maintain, and refine predictive models and machine learning algorithms to analyze large datasets from diverse sources
Develop advanced data tools and pipelines for processing, cleaning, and analyzing complex datasets, both scientific and operational (e.g., customer data, sales, financial metrics).
Utilize AI and ML techniques to drive process automation, enhance decision-making, and deliver insights for improving operational performance across marketing, sales, finance, and supply chain functions.
Infrastructure and Platform Development:
Design and implement scalable, efficient machine learning technology stack to support ongoing machine learning needs for both scientific and business operations.
Lead MLOps initiatives to streamline the model lifecycle, including continuous integration, deployment, and monitoring in production environments.
Ensure reliability and scalability of machine learning platforms through automation, adherence to industry best practices, and cutting-edge AI/ML solutions.
Collaborate with cloud teams to maintain and enhance AWS and DNAnexus platforms for seamless data integration, model deployment, and process optimization.
Business Operations Focus:
Leverage data science, AI, and ML to provide actionable insights that drive improvements in customer experience, sales forecasting, and operational efficiency.
Develop models to support business operations in areas such as demand forecasting, customer segmentation, churn prediction, and resource allocation.
Use advanced analytics to identify business trends, optimize pricing strategies, and enhance marketing campaigns.
Data Governance, Compliance, and Ethical AI:
Ensure compliance with data privacy regulations (e.g., GDPR, HIPAA) and implement strong data governance practices to ensure data security and ethical use of AI/ML models.
Advocate for responsible AI and machine learning practices, ensuring fairness, transparency, and accountability in all models deployed.
Promote best practices in ethical AI development and ensure adherence to industry standards and company policies on data usage.
Collaboration and Communication:
Work closely with molecular biologists, computational scientists, business analysts, and cross-functional teams to translate complex scientific and business problems into data science applications.
Present complex data findings and actionable insights to both technical and non-technical stakeholders, including senior leadership.
Promote knowledge sharing across departments to ensure data-driven decision-making, operational innovation, and business growth.
Collaborate with revenue, commercial, and lab product teams to analyze large datasets to extract actionable insights, identify trends, and make recommendations.
Documentation and Project Management:
Thoroughly document workflows, models, and analytical processes to ensure transparency and reproducibility.
Lead project management efforts, ensuring data science and business operations initiatives are completed on time and meet stakeholder expectations.
Qualifications:
Education:
PhD in Data Science, Bioinformatics, Computer Science, Business Analytics, or a related field with 6+ years of experience; OR
MS degree with 8+ years of experience; OR
BS degree with 10+ years of experience.
Experience:
4+ yrs experience in AWS, 2+ yrs experience in Snowflake. Sagemaker/ DataIKU / ML Flow / Meta Flow experience is a plus.
Proven experience building and deploying machine learning models, with a focus on business operations in AWS stack or snowflake. ML ops experience is required.
Proficiency in Python, and data science libraries (scikit-learn, TensorFlow, PyTorch, etc.).
Experience in Jira, github is a must. Must be familiar with Agile methodologies and be able to participate in scrums, sprint planning, sprint refinements, and retro.
Experience working with large-scale relational databases, cloud platforms (AWS, Google Cloud), and unstructured data.
Skills:
Strong understanding of AI, ML, and statistical methods applied to business data, and scientific data is a plus.
Ability to develop predictive models for customer behavior, sales forecasts, and operational efficiency.
Expertise in data mining, natural language processing (NLP), and optimization techniques.
Demonstrated leadership skills with the ability to mentor and guide junior data scientists and a small team of data scientists.
Excellent communication skills, capable of explaining technical concepts to non-technical stakeholders.
Strong organizational and project management abilities.
Preferred Skills:
Experience with time-series data, A/B testing, and predictive modeling for business functions.
Familiarity with AI and ML tools for automating business processes and improving operational efficiency.
Compensation and Benefits:
Myriad offers a competitive salary and comprehensive benefits package, including health insurance, 401(k), and opportunities for professional development.
EEO
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. In hiring and all other employment decisions, we prohibit discrimination and harassment on the basis of any protected characteristic, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. In accordance with applicable law, we make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as any mental health or physical disability needs.
#LI-Remote