Position Overview:

We’re looking for a Senior Data Implementation Engineer to help lead and accelerate Acadian’s data transformation efforts. This role is central to reimagining and rebuilding our data platform so that we can continue to scale our investment research, production data workflows, and operational capabilities in a rapidly evolving landscape.

You’ll be hands-on in designing and implementing modern data pipelines and platform capabilities using tools such as Apache Arrow, Polars, Dagster, and other emerging technologies. You’ll also help shape how we use AI-assisted development tools, such as Claude & ChatGPT, and related automation approaches, to improve the speed, quality, and maintainability of data pipeline development.

This is a senior engineering role for someone who can operate across both strategy and execution: deeply understanding existing systems, designing pragmatic transition paths, mentoring others, and building reliable, scalable, high-performance data infrastructure that directly supports Acadian’s quantitative investment process.

Acadian supports a hybrid work environment; employees are on-site in the Boston office 3 days a week.

What You’ll Do

• Lead Data Pipeline Design and Implementation
  • Design, build, and optimize robust data pipelines that are reliable, scalable, observable, and performant. Use modern data engineering tools and patterns to support investment research, production workflows, and downstream analytics.
• Drive System Modernization
  • Deeply understand existing data systems, identify architectural limitations, and lead the transition toward more modern, maintainable, and efficient data architectures.
• Shape Technical Direction
  • Partner with Data Platform Engineering, Data Analysis, and investment teams to define implementation patterns, pipeline standards, and architectural approaches that can scale across the organization.
• Apply AI Tooling to Engineering Workflows
  • Use AI-assisted development tools such as Claude, ChatGPT, or similar technologies to accelerate pipeline development, automate repetitive workflows, improve documentation, generate tests, and enhance developer productivity.
• Mentor and Lead Others
  • Provide technical guidance to junior and mid-level engineers. Help raise the engineering bar through code reviews, design discussions, pairing, documentation, and thoughtful mentorship. Lead small project teams or workstreams when needed.
• Improve Reliability and Operational Excellence
  • Develop systems with strong testing, monitoring, lineage, data quality checks, and operational supportability. Help ensure that data pipelines are resilient, transparent, and easy to maintain.
• Collaborate Across Teams
  • Work closely with stakeholders across technology, data, research, and investment teams to understand requirements, resolve ambiguity, and deliver solutions that create measurable business value.
• Continuously Improve the Platform
  • Identify opportunities to simplify, automate, and improve the data ecosystem. Champion practical innovation while balancing speed, stability, maintainability, and long-term architectural quality.

We’re Looking for Teammates With

• Bachelor’s degree in a relevant field

• A strong background in data engineering, data platform implementation, or data-intensive software engineering, ideally with 5+ years of experience in a financial, investment, or technology-driven environment.
• Deep experience building production-grade data pipelines, including ingestion, transformation, validation, orchestration, monitoring, and deployment.
• Strong Python programming skills, with relevant experience working with Dagster, Airflow or similar technologies.
• Experience with modern data architecture patterns such as data lakehouse, Medallion architecture, composable data systems, microservices, and event-driven or workflow-oriented architectures.
• Familiarity with Data Mesh concepts, including decentralized data ownership, domain-oriented data products, governance, and discoverability.
• Experience with orchestration, workflow, and data transformation tools such as dbt, Polars, Pandas, Apache Arrow, or comparable technologies.
• Practical experience using AI-assisted development tools such as Claude, ChatGPT, GitHub Copilot, or similar platforms to support data pipeline development, automation, testing, documentation, and engineering productivity.
• Demonstrated ability to mentor engineers, lead technical workstreams, and influence engineering practices across a team.
• A strong sense of ownership and the ability to operate effectively in ambiguous environments, balancing tactical delivery with long-term architectural thinking.
• A passion for building systems that are efficient, scalable, reliable, observable, and easy for others to use.
• An understanding of how data infrastructure supports the broader investment process, and a desire to deliver solutions that improve outcomes for clients and create meaningful business impact.

The base salary range for this role is $175,000 – $200,000 per year. Actual compensation will be determined based on a candidate’s skills, qualifications, and relevant experience. In addition to base pay, this position may be eligible for discretionary incentive compensation and includes participation in Acadian’s comprehensive benefits program, which includes health, retirement, and wellness offerings.

About the Role:

Stylus Medicine is seeking an intellectually curious and creative Director, who will serve as a key scientific leader on our Platform team, owning the computational strategy that drives large serine recombinase (LSR) engineering and clinical translation. This role sits at the intersection of protein engineering, multi-omics analysis, and AI/ML-driven design and will lead a high-impact team spanning NGS and computational biology while driving the analytical infrastructure and predictive capabilities needed to advance our proprietary LSR platform from discovery through translation.

Key Responsibilities & Accountabilities:

• Lead and develop a team of NGS and computational biology scientists, setting technical direction, mentoring team members, and fostering a collaborative, high-performance culture
• Lead computational efforts to design and optimize LSRs and related genome engineering proteins for genetic medicine applications, integrating sequence-function, structure-function, and protein-DNA interaction datasets to guide engineering campaigns
• Lead analysis of NGS data from LSR experiments — including amplicon sequencing, WGS, hybrid capture, and long-read sequencing — to quantify on-target efficiency and genome-wide specificity
• Build, apply, and critically evaluate predictive and generative models for protein variant and library design — including rigorous benchmarking of AI/ML approaches such as protein language models, structure prediction tools, and generative design frameworks
• Integrate proprietary screening data with public evolutionary and structural datasets to prioritize variants and identify engineering levers for activity, specificity, and developability
• Oversee the design, implementation, and deployment of robust, scalable computational pipelines for multi-omics data analysis, ensuring accuracy, reproducibility, and maintainability
• Provide scientific input into platform strategy, translating computational insights into actionable directions for the broader team
• Partner closely with the Screening, Genome Engineering, and NGS teams to design experiments, interpret complex datasets, and refine engineering hypotheses
• Contribute to patent filings, regulatory submissions, and publications

Qualifications & Skills:

• PhD in Computational Biology, Bioinformatics, Protein Engineering, Machine Learning or a related quantitative discipline, with 10+ years or Master’s with 14+ years of industry experience
• Demonstrated research in protein modeling, protein design, or computational biology through publications, preprints, or equivalent industry contributions
• Hands-on experience applying AI/ML methods to protein variant and library design, including protein language models, structure prediction, and generative design approaches (e.g., AlphaFold2/3, RFdiffusion, ProteinMPNN, ESM)
• Expert-level proficiency in Python and/or R, with deep familiarity with data science libraries (e.g., Pandas, NumPy, Bioconductor, SciPy, PyTorch, or JAX)
• Experience with cloud computing platforms and workflow management systems
• Excellent communication and documentation skills, with a track record of effective collaboration in fast-paced, interdisciplinary environments

Pay Range; $245,000 - $265,000

Role overview

The Manager, Data Science will lead an Inventory & Dealer Data Science team focused on developing, deploying, and optimizing machine learning models that power CarGurus’ products and business insights. Sitting within the broader Data Science organization, this team is responsible for modeling automotive marketplace dynamics and dealer behavior. The team owns Machine Learning solutions end-to-end across the ML lifecycle, from R&D to production. This leader is accountable for the team’s strategic direction and impactful delivery, while building a culture of innovation, technical excellence, and collaboration. The Manager partners with Product, Engineering, Analytics, and cross-functional stakeholders to ensure team efforts advance CarGurus’ goals in intelligence-powered products.

What you'll do

• Oversee the development, training, and evaluation of machine learning models covering Inventory and Dealer intelligence. Leverage modeling techniques including recommendations, demand forecasting, churn risk prediction, and valuation algorithms.
• Facilitate experimentation, A/B testing, and measurement of production models, ensuring robust evaluation of business impact
• Build and develop a high-performing team - providing regular coaching, feedback, and performance assessments; ensure equitable access to growth opportunities.
• Drive innovation, continuous improvement, and adoption of best-in-class ML and AI practices. Champion best practices across the organization.
• Participate in roadmap planning, aligning the team’s work with broader business priorities and long-term strategy. Flag resource constraints and escalate tradeoff decisions when needed.
• Act as a visible, accessible leader - communicating objectives, updates, risks, and wins to all stakeholders.
• Partner with peers in the Machine Learning Platform, Product and Data teams, and other stakeholders to facilitate effective cross-team working relationships and represent data science in cross-functional forums.

What you'll bring

• 5+ years of experience in a data science or machine learning engineering role
• 2+ years of people leadership experience
• Deep expertise in machine learning models and their application to classification, regression, ranking, and recommendation problems. Familiarity with standard evaluation metrics and statistical best practices.
• Experience deploying and owning models in production and a working understanding of MLOps practices.
• Strong working knowledge of Python development best practices and the Python ML ecosystem (e.g., scikit-learn, PyTorch, XGBoost, numpy, pandas).
• Experience with cloud platforms, particularly AWS, including deploying, monitoring, and troubleshooting ML models.
• Effective communication and facilitation abilities with both technical and non-technical stakeholders (including data scientists, product management, senior leadership, and engineers).
• Experience contributing to the technical direction for a team and a strategic mindset for prioritizing projects and aligning them with business goals.
• Knowledge of standard software development methodologies and a history of driving continuous improvement.
• Experience in a collaborative, cross-functional environment, with the ability to build effective working relationships and foster an inclusive team culture.

Layer Health was founded in 2023 by leading machine learning researchers from MIT and Harvard Medical School. We are building an AI layer that can accurately and scalably synthesize information from medical records, with the mission to reduce friction everywhere in healthcare. Our LLM-powered platform is solving chart review once and for all, across use cases. For health systems, our first product dramatically accelerates clinical registry abstraction in areas ranging from surgery and cardiology, to oncology. Our long term vision is for our AI layer to safely transform patient care and minimize unnecessary heartbreak. Layer Health’s diverse founding team brings expertise across machine learning, UI/UX, large language models, and medicine.

Here’s a collection of articles about our product, mission, recent funding round, etc.

We’re looking for a dynamic individual to join us as a Solutions Engineer, working with health system customers to pilot clinical solutions using the Layer Health platform.  Working alongside another Solutions Engineer on our team, you'll leverage your technical expertise and understanding of both healthcare workflows and healthcare integrations to collaboratively build bleeding-edge prototypes of clinical solutions and help discover the next generation of applications through participative design and iterative development with customers, including some of the largest healthcare systems in the country.  You'll collaborate closely with the Customer Success, Sales, and highly engaged Product Engineering teams to understand customer needs, rapidly prototype solutions, and successfully implement lightweight pilots. You will be instrumental in bridging the gap between clinical requirements and technical solutions for our flagship product, and you will be a pioneer for use case and product discovery for the company.

What you’ll do

• Partner with the Clinical Informatics and Customer Success teams to understand customer challenges and work collaboratively to develop and pilot clinical solutions that leverage the Layer Health platform.
• Work closely with Engineering to encourage best practices, adopt the latest developer tooling, and assess feasibility of translating proof-of-concepts into production.
• Produce documentation, including technical design and architectural diagrams, for these solutions. 
• Provide technical troubleshooting and support for clinical solutions.
• As part of the Clinical Informatics team, work to align solutions with clinical workflows, standards, and integration pipelines.
• Work closely with Product so that lessons learned from pilots translate to future product roadmaps.
• Work with Clinical Informatics to streamline the initial testing and validation of integrations with Health Systems (FHIR, HL7, etc.)
• Perform initial exploratory data analysis (EDA), working with our ML and data science teams to make sense of clinical data for downstream uses.
• Stay current with industry trends, clinical standards, and emerging technologies relevant to clinical solutions.

What you’ll need

• 3-5 years of experience deploying solutions into health care systems in a solutions engineering, clinical informatics, or technical sales role.
• Strong understanding of clinical workflows, data, and standards (e.g., HL7, FHIR).
• Fluency in Python, Pandas, standard API frameworks, and other common programming languages and libraries.
• Deep understanding of HIPAA and requirements for deploying secure solutions.
• Excellent communication, presentation, and interpersonal skills.
• Ability to work collaboratively across internal teams.
• Passion for transforming healthcare with state-of-the-art AI technology.

Expected compensation range for this role is $180,000-200,000. Compensation is dependent on experience, overall fit to our role, and candidate location. Expected compensation ranges for this role may change over time. If your compensation requirement is greater than our posted salary ranges, please still consider applying to our role. We will make a determination as to whether an exception can be made. 

If you are excited about this role, we encourage you to apply even if you don't feel that you meet every single requirement. We're eager to meet people that believe in our mission and can contribute to our team in a variety of ways. We welcome diverse perspectives, rigorous thinking, and fearlessness in challenging the status quo. 

Layer Health is committed to fostering an environment of inclusion that is free from discrimination.  We are an Equal Opportunity Employer where employment is decided on the basis of qualifications, merit, and business need. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected Veteran status, or any other characteristic protected by law.

Join us and help us transform healthcare with AI.

About the Role:

Stylus Medicine is seeking an innovative, intellectually curious and team-oriented Principal Scientist to support our large serine recombinase (LSR) engineering platform. This individual will provide high-resolution efficacy and specificity analysis to advance our pipeline as well as support onboarding of new preclinical assays for regulatory submissions, ensure accuracy of our analysis pipelines and translate our wet lab experiments into computational insights that will enable engineering of our next generation LSRs.

Key Responsibilities & Accountabilities:

• Lead the analysis of NGS data from LSR experiments (like amplicon, hybrid capture, and long-read sequencing) to quantify target efficiency and specificity
• Build integrated workflows to annotate and characterize target sites by synthesizing proprietary datasets with large-scale public genomic databases
• Design, implement, and scale robust computational pipelines for multi-omics analysis
• Collaborate closely with the Screening, Genome Engineering, and Protein Engineering teams to design experiments and interpret results
• Develop and maintain internal databases and infrastructure to ensure experimental results are accessible, reproducible, and actionable
• Contribute to patent filings, regulatory submissions, and scientific publications

Qualifications & Skills:

• PhD with 5+ years or Master’s with 10+ years (or Bachelor’s with 12+ years) of industry experience in Computational Biology, Bioinformatics, or a related quantitative discipline.
• Strong expertise in experimental design, data processing, statistical analysis, and NGS data analysis
• Expert-level proficiency in Python and/or R, with extensive experience using data science libraries (e.g., Pandas, NumPy, Bioconductor)
• Experience with cloud computing platforms for large-scale data processing and with workflow management systems
• Excellent organizational, communication, and documentation skills, with demonstrated ability to work effectively in collaborative, interdisciplinary environments
• Strong scientific critical thinking skills, including the ability to evaluate approaches, synthesize complex data, and provide actionable recommendations
• Proven track record of working successfully across cross-functional teams with proactive communication and timely delivery

Pay Range: $180,000 - $195,000