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About This Role
Manager, Clinical Data Science Syneos Health® is a leading fully-integrated life sciences services organization built to accelerate customer success. We partner with innovators at every point across the drug development and commercialization continuum, helping them navigate complexity, anticipate change and accelerate progress.
Our Clinical Solutions team members act with a drug development mindset, applying their years of experience and deep expertise to truly understand customer needs and represent those in the solutions we shape.
Whether you join us in a Functional Service Provider partnership or a Full-Service environment, you’ll collaborate with passionate problem solvers, innovating as a team to help our customers achieve their goals. We are agile and driven to deliver – for one another, our customers, and, most importantly, for those in need.
Discover what your 25,000 future colleagues already know:
Why Syneos Health
- We are passionate about developing our people, through career development and progression; supportive and engaged line management; technical and therapeutic area training; peer recognition and total rewards program.
- We are committed to building an inclusive culture – where you can authentically be yourself. Central to this is our purpose – Driven to Deliver – which captures the passion of our colleagues to show up each day and shape solutions that have the ability to dramatically impact someone’s life.
- We are continuously building the company we all want to work for and our customers want to work with. Why? Because we know that when we bring together smart colleagues from across the world, we can shape the future of healthcare, driving impact for customers and defining the pace of patient progress.
Job Responsibilities
The position is responsible for line managing, developing and being accountable for the Clinical Data
Scientists in their assigned team; client relationship management of assigned accounts; and for meeting
assigned operational targets including but not limited to quality, performance, utilization, retention, and
training. Ensures that contracted services of projects/program of projects are conducted by the Clinical
Data Science Team in accordance with the executed contract and client expectations. Supports Clinical
Data Science Business Development activities
Ensures project launch, conduct, and closeout activities are performed according to the client's
and the Company's contractual agreement. Ensures that all project-related Clinical Data Science
(CDSci) tasks are completed in accordance with internal and client quality standards, Syneos
Health SOPs, and all pertinent industry/regulatory guidelines and legal requirements. Ensures
project documentation is filed by project teams on an ongoing basis and the TMF is maintained in
an inspection ready state.
- Manages oversight of multiple projects ranging in size and complexity from single service studies
to multiple global projects within a program.
- Serves as primary escalation and relationship management point of contact for assigned client
accounts and projects. Responsible for escalation evaluation & solutions that can be solved at
Manager level, further escalating those that require additional leadership involvement.
- Accountable for financial health of assigned accounts. Oversees the functional lead financial
activity to proactively manage the scope, budget, and resources for their assigned projects.
Ensures the functional lead is appropriately and accurately forecasting future work.
- May review Clinical Data Science or other project documentation to ensure quality and
consistency across programs and teams.
- Guides functional CDSci leads to act as central stewards of clinical data quality, monitoring risks
through the holistic review of clinical and operational data, using high level knowledge of the
protocol, and considering the specific therapeutic area aspects of the protocol related to the data
collection requirements.
- Assists in the development of technology, tools or applications, for use across projects to detect
potentially unreliable data that may impact the validity of the trial results.
- Participates in submission of, analysis, and solutions for quality issues, CAPAs, and audit
findings.
Line Management and functional support of assigned staff. Interviews, onboards and trains new
staff as appropriate. Oversees professional development of direct reports by setting goals,
conducting performance reviews, evaluating and monitoring training needs, creating development
plans, mentoring, and coaching.
- Mentors staff in general team leadership skills, project management skills, internal Company
processes, business development skills, financial skills, and for project specific tasks.
- Manages the resources/workload of assigned team and projects, assigns tasks to team members,
manage billable and non-billable time, and subsequently monitors productivity/utilization,
timeliness, and quality.
- Maintains and evaluates functional lead and project progress. Works with CDSci’s to prepare for
and run Project Review Meetings for their assigned projects.
- Leads Clinical Data Science staff through client or regulatory audits. Plans for and participates in
client or regulatory audits.
- Represents Clinical Data Science at Business Development and client-related meetings. May
work with Business Development and the Proposal/Contracts groups to provide input to project
budgets and scope of work for client requested opportunities.
- May perform training for Clinical Data Science staff. Creates training materials as required.
- Participates in additional activities as assigned, such as (but not limited to): development of
departmental technologies, SOPs, processes, and procedures.
- Performs other work-related duties as assigned.
BA/BS in the biological sciences, computer sciences, mathematics, or data sciences and/or
related disciplines in the natural science/health care space. MS degree is preferred. In lieu of
degree, equivalent relevant work experience is considered.
- Strong knowledge of the drug development process and its impact on data quality, in particular
risk-based approaches, biometrics procedures, and workflows.
- Strong project management skills and knowledge of project management methodologies
- Ability to serve in a leadership role in Clinical Data Science including effective mentoring skills,
and the ability to deal effectively with clients and internal stakeholders.
- Excellent communication, presentation, interpersonal skills, both written and spoken, with an
ability to inform, influence, and persuade.
- Experience in Clinical Data Science practices and relational database management software
systems
- Demonstrated experience working within a team matrix structure.
- Demonstrated experience in making effective decisions and managing multiple priorities in a
highly dynamic environment.
- Possesses a positive attitude and works well with others.
- Knowledge of ICH/GCP regulatory requirements for clinical studies.
- Ability to travel as necessary (up to 25%)
At Syneos Health, we believe in providing an environment and culture in which Our People can thrive, develop and advance. We reward and recognize our people by providing valuable benefits and a quality-of-life balance. The benefits for this position may include a company car or car allowance, Health benefits to include Medical, Dental and Vision, Company match 401k, eligibility to participate in Employee Stock Purchase Plan, Eligibility to earn commissions/bonus based on company and individual performance, and flexible paid time off (PTO) and sick time. Because certain states and municipalities have regulated paid sick time requirements, eligibility for paid sick time may vary depending on where you work. Syneos complies with all applicable federal, state, and municipal paid sick time requirements.
Salary Range:
$95,000.00 - $210,900.00 The base salary range represents the anticipated low and high of the Syneos Health range for this position. Actual salary will vary based on various factors such as the candidate’s qualifications, skills, competencies, and proficiency for the role.
Get to know Syneos Health
Over the past 5 years, we have worked with 94% of all Novel FDA Approved Drugs, 95% of EMA Authorized Products and over 200 Studies across 73,000 Sites and 675,000+ Trial patients.
No matter what your role is, you’ll take the initiative and challenge the status quo with us in a highly competitive and ever-changing environment. Learn more about Syneos Health.
http://www.syneoshealth.com
Additional Information
Tasks, duties, and responsibilities as listed in this job description are not exhaustive. The Company, at its sole discretion and with no prior notice, may assign other tasks, duties, and job responsibilities. Equivalent experience, skills, and/or education will also be considered so qualifications of incumbents may differ from those listed in the Job Description. The Company, at its sole discretion, will determine what constitutes as equivalent to the qualifications described above. Further, nothing contained herein should be construed to create an employment contract. Occasionally, required skills/experiences for jobs are expressed in brief terms. Any language contained herein is intended to fully comply with all obligations imposed by the legislation of each country in which it operates, including the implementation of the EU Equality Directive, in relation to the recruitment and employment of its employees. The Company is committed to compliance with the Americans with Disabilities Act, including the provision of reasonable accommodations, when appropriate, to assist employees or applicants to perform the essential functions of the job.
Salary Context
This $95K-$210K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Syneos Health, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($152K) sits 28% below the category median. Disclosed range: $95K to $210K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Syneos Health AI Hiring
Syneos Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $210K - $210K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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