Direct W2 contractors only! No 3rd party agencies! No Sponsorship available
Candidate is looking for candidates within PST time zone.
Partnering Digital Solutions Join our Partnering Digital Solutions team, a strategic group focused on maximizing partnership impact through digital and AI-enabled ecosystems to advance the Pharma and DIA Partnering business. We are at the forefront of innovation - building scalable data infrastructure, applying advanced data engineering practices, and integrating cutting-edge external capabilities to accelerate decision-making and unlock business value. Our work spans across multiple high-impact initiatives, from developing intelligent data products to enabling end-to-end AI workflows. Whether it’s structuring complex data ecosystems or collaborating with external research institutions, this is a unique opportunity to help shape the future of data and AI at client by directly supporting key strategic decisions across our global Partnering organization. Responsibilities As a Data Scientist, you will play a critical role in shaping and delivering AI and machine learning capabilities across the Partnering Digital Solutions ecosystem. You will work closely with product owners, data engineers, architects, and business stakeholders to develop scalable machine learning models, intelligent workflows, and data-driven solutions that improve visibility, prioritization, operational efficiency, and strategic decision making. This role focuses on applied AI, machine learning, predictive modeling, and operational intelligence rather than purely research-oriented AI. You will help translate complex business challenges into scalable AI and analytics solutions that create measurable business impact across partnering operations. You will be responsible for:
Key Responsibilities:
● Machine Learning & Applied AI Solutions ○ Design, develop, and deploy machine learning and AI-enabled solutions that support partnering activities such as opportunity prioritization, portfolio intelligence, forecasting, operational insights, and decision support.
○ Apply advanced analytics, statistical modeling, machine learning, and emerging AI techniques to solve complex business problems and deliver scalable intelligence capabilities across the partnering ecosystem.
● Model Development & Experimentation ○ Lead exploratory data analysis, feature engineering, model selection, training, validation, and performance evaluation for machine learning and AI-enabled solutions. ○ Design and evaluate multiple modeling approaches, establish appropriate evaluation metrics, and optimize models for scalability, reliability, and business impact.
● Business Problem Solving & Decision Support ○ Partner closely with business stakeholders and product teams to translate complex business challenges into analytical approaches, machine learning solutions, and scalable intelligence capabilities. ○ Support data-driven prioritization and strategic decision making through actionable insights, predictive models, and operational intelligence.
● AI & Intelligence Enablement ○ Collaborate with AI, data, and engineering teams to operationalize AI-enabled capabilities and support the adoption of scalable intelligence solutions across partnering platforms and workflows. ○ Evaluate and apply modern AI techniques including predictive modeling, NLP, LLM-enabled workflows, and intelligent automation approaches to enhance partnering operations and decision making.
● Data & Intelligence Development ○ Design and develop analytics solutions, dashboards, KPIs, and intelligence capabilities that improve visibility into partnering operations, opportunities, and portfolio activities. ○ Support development of operational intelligence capabilities that enable proactive and informed business decisions.
● AI Operationalization & MLOps ○ Collaborate with engineering and architecture teams to support operationalization of machine learning and AI-enabled solutions in production environments. ○ Support scalable deployment, monitoring, observability, and lifecycle management of AI and machine learning capabilities aligned with enterprise AI standards and governance practices.
● Data Storytelling & Communication ○ Communicate analytical findings, model outputs, and AI-driven insights through clear visualizations, presentations, and storytelling that support business understanding and stakeholder decision making. ○ Translate complex analytical and machine learning concepts into practical business insights for both technical and non-technical audiences.
● Collaboration & Continuous Improvement ○ Work in a cross-functional Agile environment and collaborate closely with product owners, data engineers, architects, vendors, and business stakeholders to continuously improve AI capabilities, analytics solutions, and operational intelligence across PDS. ○ Contribute to evolving AI practices, engineering standards, experimentation frameworks, and continuous improvement initiatives across the organization.
Skills require:
● BA, Master’s degree or PhD in Data Science, Computer Science, Statistics, Mathematics, Engineering, Artificial Intelligence, or a related quantitative field.
● 5+ years of experience in data science, applied AI, machine learning, or advanced analytics solution development.
● Strong experience designing, developing, evaluating, and deploying machine learning models for business applications.
● Strong proficiency in Python and machine learning/data science frameworks such as scikit-learn, TensorFlow, PyTorch, pandas, and NumPy. ● Strong SQL skills for querying, transformation, and analysis of large datasets.
● Experience building predictive models, classification models, recommendation systems, forecasting models, or decision-support solutions.
● Experience working with structured and unstructured data from multiple sources. ● Familiarity with cloud-native AI and analytics platforms such as AWS, GCP, or Azure.
● Strong communication and collaboration skills with the ability to work across technical and business teams.
Desired skills:
● Experience developing applied AI or machine learning solutions that support operational or business workflows.
● Experience working with LLMs, NLP techniques, vector databases, or AI-enabled workflow solutions.
● Familiarity with agentic AI workflows, orchestration frameworks, or intelligent automation approaches.
● Experience operationalizing ML/AI models in production environments. ● Familiarity with MLOps or LLMOps concepts including deployment, monitoring, orchestration, observability, and model lifecycle management.