About the Position

We're looking for a Senior Data Engineer to join the Scientific Data Intelligence (SDI) team at Formation Bio to help transform Real World Data (RWD)—spanning electronic health records, claims, and other longitudinal patient data sources—into structured, analytics-ready assets. In this role, you'll be partnering closely with our Data Science team not only to model and transform data, but also to actively analyze it: answering research questions, generating evidence, and supporting scientific decision-making across our drug portfolio.

This position sits at the intersection of healthcare data engineering, real-world evidence analysis, and generative AI. While a strong foundation in building reliable, scalable pipelines is essential, you'll be equally expected to roll up your sleeves and work directly with the data—constructing cohorts, running analyses, and translating findings into actionable insights for scientific and business stakeholders.

The ideal candidate is a hybrid of data engineer and applied scientist: someone who can build the infrastructure and then use it, with familiarity in RWD study design, GenAI fluency (e.g., LLM-based entity extraction, summarization, classification), and strong technical expertise with modern data tooling. You'll play a key role in shaping how real-world patient data becomes discoverable, structured, and impactful across the organization.

Responsibilities

• Model and transform raw EHR and claims data into clean, canonical, and analytics-ready datasets using SQL, Python, and clinical standards like OMOP.
• Build and manage scalable data pipelines using Dagster for orchestration, dbt for transformation, and Snowflake as the primary compute and storage engine.
• Conduct hands-on RWD analyses to answer scientific and strategic research questions—including disease epidemiology, treatment patterns, patient journey characterization, and comparative effectiveness.
• Partner with Data Scientists and clinical leads to design and execute observational studies, translating scientific questions into well-structured, reproducible analyses.
• Implement data validation, completeness, and observability frameworks to ensure real-world datasets are accurate, comprehensive, and trustworthy for downstream research and product use.
• Apply Generative AI techniques within transformation and analysis layers to accelerate data structuring and insight generation.
• Communicate findings clearly to both technical and non-technical stakeholders, including summaries for portfolio teams and leadership.

About You

• You have 5+ years of experience in data engineering, ideally with at least 2 years working in healthcare or life sciences, including direct exposure to EHR or claims datasets.
• You have experience with ontologies and biomedical schemas (e.g. UMLS, LOINC, ICD9/10, MeSH) and understand the modalities found within RWD — billing claims, lab results, visit notes.
• You're fluent in SQL and Python, and you've built and maintained production-grade pipelines that support analytics or scientific workflows.
• You have experience building longitudinal patient cohorts from EHR or claims data, including index date logic, washout periods, and follow-up window construction.
• You have a solid understanding of the causal inference frameworks such as potential outcomes and target trial emulation. 
• You have working familiarity with real-world evidence study design concepts—such as active comparator new user designs, time-to-event outcomes, confounder adjustment, and causal discovery algorithms—sufficient to partner effectively with Data Scientists on causal inference workflows.
• You value clarity, documentation, and structured thinking—especially when working with complex healthcare data.
• You have hands-on expertise with modern data infrastructure, such as Snowflake, dbt, and Dagster.
• You can balance upfront design with speed to execution, slowing down when it counts without getting stuck in the details.
• Bonus: You've worked in regulated or privacy-sensitive data environments and are familiar with governance models for PHI or sensitive data.
• Bonus: You have prior experience working with commercial RWD vendors (e.g. Truveta, Optum, Komodo, IQVIA) and understand the nuances of licensed claims and EHR datasets, including longitudinal patient journey construction and line-of-therapy sequencing.

Total Compensation Range: $204,500 - $267,000

Who We Are:

At Bandwidth, your music matters when you are part of the BAND.  We celebrate differences and encourage BANDmates to be their authentic selves.  #jointheband

What We Are Looking For:

A Sr. Data Scientist serves as a technical authority and strategic leader within the Data Science team, driving the highest-impact data science initiatives across Bandwidth. They own and lead complex, ambiguous end-to-end data science projects, from opportunity identification and problem framing through model deployment and business adoption, ensuring measurable impact on revenue, efficiency, and competitive differentiation. They act as a force multiplier by setting technical standards, shaping data science strategy, and elevating the capabilities of the broader team through mentorship, code review, and thought leadership. They also partner with senior executives, product leadership, and cross-functional teams to embed data-driven decision-making into Bandwidth's core business processes and long-term strategy.

What You'll Do:

• Drive end-to-end ownership of high-complexity, high-visibility data science projects, independently navigating ambiguity from problem definition through production deployment and business impact measurement.
• Architect and implement advanced machine learning systems, including supervised and unsupervised learning, deep learning, ensemble methods, and large-scale optimization, applying rigorous evaluation to ensure production-grade reliability and performance.
• Define and champion best practices for model development, validation, deployment, and monitoring; establish technical standards that elevate quality and consistency across the team.
• Translate ambiguous, strategic business challenges into well-scoped data science problems; lead discovery processes with senior stakeholders to align on objectives, constraints, and success criteria.
• Deliver executive-level data-driven narratives that communicate the strategic value of data science, influencing senior leadership and cross-functional decision-makers to drive resource allocation and organizational alignment.
• Serve as a technical lead and primary mentor for intermediate and junior data scientists, conducting code reviews, establishing growth plans, and fostering a high-performance team culture.
• Proactively identify emerging opportunities to apply new techniques, including cutting-edge research, novel architectures, and industry-leading tools to advance Bandwidth's competitive position.
• Partner with data engineering, software development, and platform teams to design scalable data and AI infrastructure that enables reliable, high-throughput machine learning workflows at enterprise scale.
• Oversee model performance, data quality, and system health in production environments; lead root-cause analysis and implement robust solutions for drift detection, retraining, and performance degradation.
• Lead the design and implementation of CI/CD pipelines for AI projects, enabling rapid iteration, reproducibility, and safe deployment practices.
• Represent the Data Science function in cross-departmental working groups, product planning sessions, and strategic initiatives requiring deep analytical expertise.

What You Need:

• Education:  
  • Bachelor's degree in Mathematics, Statistics, Computer Science; graduate degree in Data Science or other quantitative field is preferred.
• Experience: 
  • 7+ years of directly related data science experience, with a demonstrated record of independently delivering impactful, production-grade ML/AI solutions; or a graduate degree plus 5+ years of equivalent experience.
  • Prior experience in a technical lead or informal management capacity is strongly preferred.
• Skills:
  • Expert-level proficiency in Python, with deep knowledge of the data science and ML ecosystem including pandas, NumPy, scikit-learn, TensorFlow, PyTorch, and MLflow or equivalent MLOps tooling.
  • Advanced mastery of statistical inference, experimental design, and machine learning, including hypothesis testing, causal inference, regression, classification, clustering, deep learning architectures, and model evaluation at scale.
  • Expert knowledge of data structures, SQL, and distributed data processing frameworks (e.g., Spark, BigQuery, Snowflake); strong familiarity with REST APIs and cloud-based ML platforms (AWS, GCP, or Azure).
  • Demonstrated ability to independently define, scope, and deliver solutions to ambiguous, high-stakes business problems through data mining, analytics, statistical modeling, and predictive systems.
  • Exceptional communication and executive presence; proven ability to convey complex technical concepts and strategic insights to product leadership and non-technical audiences in a compelling, business-focused manner.
  • Strong leadership and organizational skills with a track record of mentoring teams, driving cross-functional alignment, and managing multiple high-priority initiatives in a fast-paced environment.
  • Proven ability to influence without formal authority, building consensus and driving adoption of ML/AI solutions across diverse stakeholders.

Bonus Points:

• Familiarity with big data technologies such as Kafka, Flink, Hadoop, Spark, or distributed computing frameworks for processing and analyzing large-scale datasets efficiently.
• Experience with MLOps tooling and production ML system design, including feature stores, model registries, and monitoring frameworks.
• Experience in telecommunications, SaaS, or communications technology industries.

The Whole Person Promise:

At Bandwidth, we’re pretty proud of our corporate culture, which is rooted in our “Whole Person Promise.” We promise all employees that they can have meaningful work AND a full life, and we provide a work environment geared toward enriching your body, mind, and spirit. How do we do that? Well…

• 100% company-paid Medical, Vision, & Dental coverage for you and your family with low deductibles and low out-of-pocket expenses.
• All new hires receive four weeks of PTO.
• PTO Embargo. When you take time off (of any kind!) you’re embargoed from working. Bandmates and managers are not allowed to interrupt your PTO – not even with email.
• Additional PTO can be earned throughout the year through volunteer hours and Bandwidth challenges.
• “Mahalo moments” program grants additional time off for life’s most important moments like graduations, buying a first home, getting married, wedding anniversaries (every five years), and the birth of a grandchild.
• 90-Minute Workout Lunches and unlimited meetings with our very own nutritionist.

At Target RWE, our mission is driven by a deep commitment to people, whether it’s the patients we serve, the partners we collaborate with, or the dedicated team members who power our work. As a purpose-driven organization, we leverage real-world data to advance clinical research and inform better healthcare decisions. We foster a collaborative environment where every voice is heard, every idea has an impact, and every contribution helps improve lives. If you're seeking a place where your work truly matters, join us to advance science.

Overview

In this role, you will be a foundational member of Target RWE’s Quantitative Sciences (QS) organization, helping to establish Target as a leader in rigorous, reproducible, and regulatory-grade evidence generation. You will serve as a scientific owner of Target’s data, developing deep expertise in its structure, quality, and applications while helping to build scalable data validation, curation, and analytic processes that ensure reliability and usability.

Working closely with cross-functional partners in Product, Engineering, Clinical Operations, and Medical Science, you will design and execute analyses that demonstrate the value of Target’s data, support customers across life sciences, regulatory, and academic settings, and build scalable, standardized analytic approaches for real-world evidence generation.

What You’ll Do

1. Data Ownership and Quality 

• You will establish Target’s data as a trusted, validated scientific asset.
• Ensure Target’s data are accurate, complete, and fit for regulatory and scientific use by building systems and practices that establish trust in data integrity and readiness for analysis.
• Develop deep expertise in Target’s data, understanding its structure, provenance, strengths, and limitations.
• Design and implement validation frameworks, data quality checks, and “fitness-for-use” assessments.
• Create and maintain data dictionaries and documentation that promote transparency and reproducibility.

2. Analytical excellence 

• You will apply advanced analytic methods and scientific rigor to generate reliable, reproducible, and regulatory-grade real-world evidence from Target’s data assets.
  • Support the design and execution of retrospective and prospective studies using Target’s data assets.
  • Develop statistical analysis plans and analytic code for descriptive, comparative, and predictive analyses.
  • Design and implement scalable, reusable analytic frameworks and modular code structures that support consistent execution across therapeutic areas and data products.
  • Apply best practices in methodology, version control, code review, and documentation to ensure reproducibility.
  • Translate complex results into clear visualizations and insights for scientific, regulatory, and client audiences.
  • Contribute to publications, abstracts, and collaborations that advance Target’s scientific reputation and influence.

3. Cross functional partnership

• You will work cross-functionally to connect data, science, and strategy, ensuring Target’s evidence generation aligns with customer needs and business goals.
• Partner closely with Commercial, Medical Science, Product, Data Insights, Engineering, and Clinical Operations teams to align on data strategy and analytic priorities.
• Serve as the internal “voice of the customer,” anticipating how clients, regulators, and researchers will use Target’s data.
• Participate in client and partner meetings to demonstrate the validity and value of Target’s data products.
• Leverage your deep knowledge of Target’s data assets to guide internal teams and external partners, anticipating analytical and operational challenges and helping to shape data use strategies.
• Translate analytic findings and data characteristics into clear, client-facing materials that demonstrate Target’s data quality, representativeness, and scientific utility.

What You’ll Bring

• Advanced degree (MS or PhD) in biostatistics, epidemiology, health economics, bioinformatics, data science, or a related quantitative field. 
• 5-10+ years of experience working with real-world data (e.g., EMR, claims, registry, or clinical trial data).
• Strong programming expertise in R, and experience with reproducible analytic frameworks.
• Proven expertise in data quality assessment, validation, and curation workflows.
• Demonstrated ability to build and maintain reproducible coding practices and scalable analytic processes that enable efficiency, transparency, and consistency across studies.
• Strong communication, presentation and collaboration skills with cross-functional technical and scientific teams.
• Demonstrated ability to translate data insights into actionable scientific or business recommendations.

What we offer you

• Hybrid work — 3 days/week in our brand-new office!
• Comprehensive health, dental, and vision for you and your family
• 401(k) with company match
• Generous PTO and company holidays
• Paid parental leave