This role will report to the Lead, Advanced Analytics within the I&A organization, the Senior, Advanced Analytics Engineer will bring deep technical expertise in advanced analytics and machine learning techniques to deploy, monitor, and maintain models through our Databricks cloud platform, while collaborating with senior data scientists, operational leaders, and clinical staff on high-impact data products and use cases. You will act as a thought leader, promoting best practices to enable teams across the institute to perform efficient and effective analytics.ÂYour work will directly influence the deployment of cutting-edge solutions that drive real-world outcomes for cancer treatment and research.Â

Located in Boston and the surrounding communities, Dana-Farber Cancer Institute is a leader in life changing breakthroughs in cancer research and patient care. We are united in our mission of conquering cancer, HIV/AIDS and related diseases. We strive to create an inclusive, diverse, and equitable environment where we provide compassionate and comprehensive care to patients of all backgrounds, and design programs to promote public health particularly among high-risk and underserved populations. We conduct groundbreaking research that advances treatment, we educate tomorrow's physician/researchers, and we work with amazing partners, including other Harvard Medical School-affiliated hospitals.

Model Creation, Deployment, and MaintenanceÂ

• Plan, advise and execute on scalable practices for development, deployment, and long-term monitoring for advanced analytics solutions, ensuring a delivery that is built for scale, reliable telemetry tracking, informative monitoring and diagnosis of model drift, and cross-platform efficiency.
• Implement and maintain best-in-class data solutions, managing machine learning models from deployment to retirement, ensuring models are well organized, auditable, and continuously perform with the highest degree of accuracy.
• Develop CI/CD pipelines for predictive and forecasting models in cloud environments, including batch, online, streaming, and edge training/inference.
• Elicit functional requirements from end users, advanced analytics, and data science teams, utilizing methods such as user interviews, mockups, wireframes, end-to-end testing, prototypes, GUI designs, use cases, and surveys of your communities of practice.
• Establish yourself as an Institute Analytics thought leader, bringing expertise and impeccable communication on what’s possible and not possible with these capabilities, including knowledge of recent advancements, established methods, the pathways to success, and helping staff to see the “art of the possible”.

Data Architecture and Accessibility

• Oversee the data governance, including data versioning, lineage, and compliance, ensuring model components and model data are available for high-throughput, low-latency processing and training.
• Design, implement, and maintain data pipelines that support machine learning models, ensuring data availability and integrity across hybrid cloud platforms.
• Collaborate on multi-cloud & hybrid data platforms (e.g., Databricks, Snowflake, Kubernetes, Spark) to support machine learning and AI initiatives.

Thought Leadership, Best Practices, and User Enablement:

• Provide thought leadership in how analytics platforms can be used and measured to achieve best practices in performance, reliability, usability, and value for users.
• Communicate status on various project/program efforts to multiple groups including the Advanced Analytics team, project sponsors, business owners, and internal stakeholders.
• Embed continuous improvement processes in your product solution designs, ways of working, and the solution consulting practice you build ensuring durable products that provide long-term value.
• Mentor and provide guidance to junior and new team members.

• Bachelor’s degree in Computer Science, Information Systems, or related field (Master’s or PhD preferred).
• 5 years of relevant experience in advanced analytics leveraging machine learning techniques, model deployment. 2+ years in healthcare/life sciences (clinical or research setting) preferred.
• High proficiency with using Python, SQL and multi-cloud & hybrid data platforms (Databricks, Snowflake, Docker/Spark/Kubernetes).Experience with data engineering, MLOps, and deploying ML models in cloud environments (e.g., Azure, AWS, GCP).ÂFamiliarity with Infrastructure as Code (IaC) and DataOps practices, including tools like Terraform and Jenkins.Â
• Experience designing technical solutions in partnership with development teams for monitoring and management of large-scale custom software applications.Â
• Familiarity with healthcare data and regulatory environments (HIPAA, GDPR) is preferred.Â

KNOWLEDGE, SKILLS, AND ABILITIES REQUIRED:

• Proven ability to handle high-throughput big data processing with low-latency requirements.
• Advanced understanding of the AI/ML lifecycle, including data preparation, model deployment, monitoring, and lifecycle management.
• Advanced understanding of software development and platform management and ability to adapt quickly to changing requirements.
• Excellent understanding in best practices for solution design for data, AI, informatics, and/or software development use cases including technical architecture, and the interactions between systems including working knowledge of data platforms, the AI tech stack, and the software development lifecycle.
• Work closely with stakeholders across the Institute to understand data and AI needs and deliver tailored technical solutions.
• Act as a liaison between advanced analytics teams and operational leaders, ensuring that machine learning models are effectively deployed and managed.
• Provide exceptional problem-solving and communication skills, working with cross-functional teams to meet the evolving needs of the Institute’s AI initiatives.
• Demonstrated ability to provide exceptional user support, meet users where they are, talk their language, and guide them to expand their capabilities beyond what they thought they were capable of.
• Able to skillfully educate the business on the importance of leveraging data & analysis for decision-making and enable the embedding of a data-driven culture.
• Ability to work and maintain focus within a complex, fast-paced work environment with multiple concurrent, inter-related subprojects, as well as shifting and competing priorities. Leads technical discussions and report project status to stakeholders and collaborators.
• Ability to skillfully and routinely create, cultivate, and leverage cross-group, cross-departmental (e.g., I.S.), and external (e.g., vendor) relationships where appropriate with a big picture view of DFCI benefit.
• Possesses a high-level of empathy, kindness, and a mindset of inclusive, diverse thinking.
• Demonstrates self-awareness, professionalism, agility and flexibility, a strong work ethic, appropriate humility, and the ability to lead through challenging situations.
• Ability to think and act consistently with a broader “we” mentality, i.e., what’s best for the group as a whole vs. self and “my team”.

At Dana-Farber Cancer Institute, we work every day to create an innovative, caring, and inclusive environment where every patient, family, and staff member feels they belong. As relentless as we are in our mission to reduce the burden of cancer for all, we are equally committed to diversifying our faculty and staff. Cancer knows no boundaries and when it comes to hiring the most dedicated and diverse professionals, neither do we. If working in this kind of organization inspires you, we encourage you to apply.

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Dana-Farber Cancer Institute is an equal opportunity employer and affirms the right of every qualified applicant to receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service, protected veteran status, or other characteristics protected by law.

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