Sr. Manager, Data Scientist - Parsippany, NJ

Descripción del trabajo

At Gilead our pursuit of a healthier world for all people has yielded a cure for hepatitis C, revolutionary improvements in HIV

treatment and prevention as well as advancements in therapies for viral and inflammatory diseases and certain cancers.

We set and achieve bold ambitions in our fight against the world’s most devastating diseases, united in our commitment to

confronting the largest public health challenges of our day and improving the lives of patients for generations to come. As a

Sr. Manager, Data Scientist, at Gilead you will ...

The Senior Data Scientist role is a critical role within Team ARC (AI Research Center), part of the Clinical Data Science (CDS) organization within Gilead’s Drug Development division. This position is responsible for driving innovation and delivering scalable, production-ready fit for purpose AI solutions to accelerate drug development, improve operational workflows, and create measurable business value.

You will lead design, development and implementation of data science solutions to support Development and other business decision-making, ensuring compliance with data quality and data governance standards. You will develop and utilize a comprehensive and deep understanding of data and foster learning using data engineering, automation and visualization tools and applications to broaden efficiency with data accessibility, while enabling a culture of data-driven decision-making.

RESPONSIBILITIES:

· Plays a key role in the ongoing continuous improvement of descriptive statistics, data engineering, model building and evaluation, prediction, visualization and automation for business use.

· Plays a lead role in development of team and department-level standards, tools and templates.

· Provides matrix leadership to small teams in the development and maintenance of data science processes and applications.

· Defines and designs the overall data science solution architecture for assigned groups or projects.

· Coordinates infrastructure requirements with the business and IT to support the data science solution architecture.

· Designs data science workflows for the assigned area, incorporating best practice statistical analysis, data engineering, automation and visualization, deep learning and other contemporary machine learning techniques, models and tools.

· Builds, implements, tests, deploys and maintains innovative data and Machine Learning solutions to improve clinical trials and other business operations.

· Performs statistical analysis of results to tune / refine Machine Learning models.

· Collaborates with scientists, engineers, product managers and other business stakeholders to design and implement software solutions.

· Uses Machine Learning best practices to ensure a high standard of quality for all deliverables.

· Creates and manages resource plans for assigned work. Identifies cross-project synergies to leverage efficiencies and ensure consistencies where appropriate.

· Ensures assigned work complies with established practices, policies and processes and any regulatory or other requirements.

Basic Qualifications:

BS with 8+ Years of experience

OR

MS with 6+ Years of experience

OR

PhD/PharmD with 0 Years of experience

Preferred Qualifications:

• PhD/PharmD with 4 Years of experience is preferred.

• An academic focus on Artificial Intelligence / Machine Learning.

• Significant experience leading data science projects and project teams with a minimum of 2 years’ cross-functional project management or leadership experience.

• Significant experience in end-to-end data science techniques, including data engineering, statistical analysis, modeling, visualization, presentation and warehousing.

• Significant experience building Machine Learning models and libraries.

• Strong programming skills in key languages used by Data Science, e.g., Python, SQL etc., with proven capabilities to manipulate large and sophisticated datasets using distributed computing technologies.

• Significant experience engineering and architecting data lakes, data warehouses and big data storage and platforms on AWS.

• Significant experience working with modern high-performance columnar storage formats.

• Strong proficiencies in problem-solving, algorithm design and complexity analysis.

• Significant experience contributing to open-source projects.

• Experience training others in data science principles, practices and tools.

• A track record in publications within own field is strongly preferred.

Knowledge & Other Requirements

· Has advanced knowledge in two or more key areas, such as statistical analysis, data automation, data mining and/or data visualization, as evidenced by increasing independence in managing data science deliverables.

· Has advanced knowledge of data science best practices and tools and has a track record of applying this to significantly improve decision-making effectiveness and efficiencies.

· Demonstrated ability to be a fast learner.

· Demonstrated ability to be flexible and adaptable to change, to move between projects easily and provide support/expertise where needed.

· Proven analytical abilities with high attention-to-detail as demonstrated through past experiences and/or academic achievements.

· Strong knowledge of software development methodologies and tools.

· Strong knowledge of modern Machine Learning architectures, platforms and backend systems. Proven ability to develop highly complex Machine Learning models for automated data analysis.

· Has advanced knowledge of cloud Machine Learning services.

· Proven ability to conduct effective and efficient exploratory analysis on large volumes of data and identify key descriptive and inferential properties.

· Demonstrated mindset of commit early and often, metrics before models, and shipping high-quality production code.

· Strong communication and organizational skills.

· Proven effectiveness leading and influencing programs, projects and / or initiatives.

· Strong leadership presence with demonstrated ability to lead without authority and influence programs, projects and/or initiatives.

· When needed, ability to travel.

People Leader Accountabilities:

•Create Inclusion - knowing the business value of diverse teams, modeling inclusion, and embedding the value of diversity in the

way they manage their teams.

•Develop Talent - understand the skills, experience, aspirations and potential of their employees and coach them on current

performance and future potential. They ensure employees are receiving the feedback and insight needed to grow, develop and

realize their purpose.

•Empower Teams - connect the team to the organization by aligning goals, purpose, and organizational objectives, and holding

them to account. They provide the support needed to remove barriers and connect their team to the broader ecosystem.