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About This Role
The American Diabetes Association (ADA) is seeking a Managing Director, Data and AI to oversee the development and execution of the organization's data strategy, ensuring the effective use of data and AI technologies to support business objectives. The Managing Director will lead and manage data and AI initiatives, support the development of our internal data infrastructure and drive the adoption of artificial intelligence technologies to enhance decision\-making and operational efficiency. This role requires a strategic thinker with a deep understanding of data science, data management, analytics, machine learning, and artificial intelligence (AI), as well as strong leadership and management skills. The ideal candidate is a self\-starter with an entrepreneurial mindset and the drive to get things done with minimal supervision combined with the humility to recognize that the most technically correct solution may not be the most appropriate solution to address a particular challenge. This position reports directly to the SVP of Information Technology and Services. This Managing Director of Data and AI will ideally be based in the DMV region, but we are open to other regions for the right candidate.
Responsibilities
- Work collaboratively with division heads to develop and implement the company's data strategy
- Establish a vision for data and AI initiatives, aligning them with business goals and objectives.
- Assist in the implementation of a data lake and data warehouse, one lake, data pipelines, ETL and ELT.
- Act as an internal consultant to bridge the gap between the technical and business sides
- Oversee data governance, data quality, data integration, data analytics and enterprise data applications support.
- Drive innovation: Identify and explore new opportunities for leveraging data and AI to create competitive advantages and improve business outcomes.
- Collaborate with cross\-functional teams: Work closely with other departments to integrate data and AI solutions into various business processes.
- Oversee the development and maintenance of data systems and platforms to support analytics and AI initiatives.
- Monitor industry trends: Stay informed about the latest advancements in data science and AI and apply relevant insights to the company's strategy.
- Ensure compliance with regulations: Adhere to data privacy and security laws and standards, ensuring the responsible use of data.
*Data Strategy and Management Duties*
- Develop and execute a comprehensive short\- and long\-term data strategy aligned with the organization's goals and objectives.
- Collaborate in the design and implementation of the organizational data infrastructure, in particular the data warehouse and data lake.
- Proactively work with various divisions to determine available data sources, priorities for ingesting into the data lake.
- Create organizational wide ontologies and data cataloging strategies.
- Oversee the collection, storage, management, and analysis of data to ensure data quality and integrity.
- Implement data governance frameworks and best practices to maintain data security and compliance with relevant regulations.
- Collaborate with cross\-functional teams to identify data needs and provide insights for business growth.
- Develop strong collaborative work relationships with divisions to identify opportunities for data management tools.
*AI Implementation and Optimization Duties*
- Lead the development and deployment of AI\-driven solutions to enhance business processes and constituent experiences.
- Evaluate and select AI technologies and tools that align with the organization's needs and capabilities.
- Monitor and optimize AI models and algorithms to ensure their accuracy, efficiency, and scalability.
- Stay updated with the latest advancements in AI and data science to drive continuous innovation.
*Team Leadership and Collaboration Duties*
- Establish, manage and mentor a team of data scientists, analysts, and AI specialists to achieve project goals and objectives.
- Foster a collaborative and innovative team culture, encouraging knowledge sharing and professional growth.
- Coordinate with other departments to promote data\-driven decision\-making and integrate AI solutions across the organization.
- Present findings, insights, and recommendations to senior management and stakeholders.
Qualifications
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Artificial Intelligence, or a related field.
- At least 7 years of experience in data science, machine learning, or AI, with a minimum of 5 years in a leadership role.
- Technical skills: Proficiency in data analysis, data management, machine learning algorithms, AI frameworks, big data concepts and tools, and data visualization tools (Tableau, Power BI, AWS Quicksight, etc.)
- Leadership skills: Proven ability to lead and manage a team, with strong communication and interpersonal skills, with the ability to collaborate effectively with cross\-functional teams.
- Strategic thinking: Ability to develop and execute a data strategy that aligns with business objectives.
- Problem\-solving: Strong analytical and problem\-solving skills, with the ability to translate complex data into actionable insights.
- Industry knowledge: Familiarity with industry trends and best practices in data science and AI.
- Strong understanding of data modeling, warehousing, and processing.
- In\-depth experience with integrating data from different sources.
- Strong knowledge of data governance, data quality, and data security practices.
- Proficiency in programming and data querying languages such as Python, R, Perl and SQL.
- Experience with data lake and data warehouse tools and concepts (e.g. AWS, Snowflake, MuleSoft, Delta Lake/Databricks, AI/ML data star schemas, denormalization strategies, etc.)
- Experience with AI and machine learning frameworks such as TensorFlow, PyTorch, and scikit\-learn.
- Demonstrated experience with data cataloging and dealing with and improving data quality.
- Advanced proficiency with Microsoft Office Suite more specifically, Office 365\.
- Strong project management skills, with the ability to prioritize tasks and manage multiple projects simultaneously and a proven track record of leading data and AI projects from inception to completion.
Why Work Here
The American Diabetes Association ® (ADA) offers a rewarding career working for one of the premier voluntary health organizations in the world supporting people living with diabetes and/or obesity. Our employees consistently say that our mission, culture, work\-life balance, and total rewards package are what they like most about working at the ADA. benefits and our culture:
- Industry competitive base pay. Base offers are determined by several factors including but not limited to your relevant work experience, education, certifications, location, internal pay equity, etc.
- A culture of recognition including new hire welcome announcements, service anniversary awards, referral bonuses, monthly All Employee Assembly, appreciation awards
- Generous Paid Time Off, including holidays, vacation days, personal days and sick days
- Comprehensive benefits package including medical, dental, vision, Flexible Spending Accounts (FSA), disability \& life insurance, pet insurance, and retirement savings
- Guided by our mission, we provide full diabetes supply coverage through our medical benefits program
- A company focus on offering mental health programs and work/life balance with most of our employees working remote
- Joining our dedicated team affords the gratification of knowing beyond a doubt that you will impact the lives and well\-being of millions
About the Organization
The American Diabetes Association ® (ADA) is the nation’s leading voluntary health organization fighting to end diabetes and helping people living with diabetes thrive. Since 1940, we have driven discovery and research to prevent, manage, treat, and ultimately cure diabetes. We advocate for policy changes and improved access to quality care for all people living with diabetes and/or obesity. Through the Obesity Association, a division of the ADA, we are also working to reduce the prevalence of obesity—a leading risk factor for type 2 diabetes.
Salary Context
This $126K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At American Diabetes Association, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($143K) sits 33% below the category median. Disclosed range: $126K to $160K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
American Diabetes Association AI Hiring
American Diabetes Association has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $160K - $160K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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