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About This Role
Location: This is a hybrid remote/in\-office role.
Medidata follows a hybrid office policy in which employees who are hired for an in\-person position are expected to work on site a certain number of days per week following Company policy.
About our Company:
Medidata is powering smarter treatments and healthier people through digital solutions to support clinical trials. Celebrating over 25 years of ground\-breaking technological innovation across more than 38,000 trials and 12 million patients, Medidata offers industry\-leading expertise, analytics\-powered insights, and one of the largest clinical trial data sets in the industry. More than 1 million registered users across approximately 2,300 customers trust Medidata's seamless, end\-to\-end platform to improve patient experiences, accelerate clinical breakthroughs, and bring therapies to market faster. A Dassault Systèmes brand (Euronext Paris: FR0014003TT8, DSY.PA), Medidata is headquartered in New York City and has been recognized as a Leader by Everest Group and IDC. Discover more at www.medidata.com. Listen to our latest podcast, from Dreamers to Disruptors, and follow us at @Medidata.
About the Team:
The Senior Manager, Data Transformation will serve as the strategic and operational lead for the data, analytics, and reporting function supporting the FP\&A organization. This is an individual contributor role responsible for the end\-to\-end data architecture, day\-to\-day reporting operations, and the long\-term transformation roadmap that enables Finance to deliver faster, more accurate, and more insightful business partnership.
The ideal candidate is a proactive, hands\-on practitioner who blends finance domain knowledge with strong data analysis, business intelligence, and process transformation capabilities. They will anticipate stakeholder needs, identify opportunities without being asked, and partner closely with FP\&A, Accounting, IT, and Data Engineering teams to modernize how financial data flows, is modeled, analyzed, and consumed across the enterprise — driving a shift from manual, spreadsheet\-based reporting to scalable, automated, self\-service analytics.
Responsibilities:
Data Strategy \& Transformation Leadership
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- Define and execute the multi\-year data and reporting transformation roadmap for FP\&A, aligned with broader Finance and enterprise data strategy.
- Lead the design and evolution of the FP\&A data architecture, including data sources, integration layers, financial data models, and the semantic / reporting layer.
- Partner with IT, Data Engineering, and Enterprise Data teams to ensure FP\&A requirements are represented in enterprise data platforms (e.g., data warehouse, lakehouse, master data, ERP).
- Champion a shift from manual, spreadsheet\-driven processes toward automated, governed, and scalable reporting and planning solutions.
- Build the business case for transformation initiatives, including ROI, resource needs, and change management considerations.
Day\-to\-Day Data \& Reporting Operations
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- Own end\-to\-end delivery of recurring FP\&A reporting cycles, including monthly close reporting, management reporting packages, board materials, and ad hoc executive requests.
- Ensure the accuracy, timeliness, completeness, and consistency of all financial data and reports consumed by FP\&A and business stakeholders.
- Establish and maintain SLAs, controls, and reconciliation processes between source systems (e.g., ERP, CRM, HRIS) and the FP\&A reporting environment.
- Serve as the escalation point for data issues, reporting discrepancies, and stakeholder requests, driving root\-cause analysis and durable fixes.
- Maintain documentation, data dictionaries, and lineage for key FP\&A datasets, metrics, and KPIs.
- Serve as the business owner and administrator of the FP\&A forecasting / planning tool, including model maintenance, hierarchy and metadata updates, user access, integrations, and ongoing enhancements to support planning and forecasting cycles.
Process Transformation \& Automation
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- Identify and prioritize opportunities to streamline, standardize, and automate FP\&A processes across planning, forecasting, reporting, and analytics.
- Lead the design and implementation of automated data pipelines, ETL/ELT workflows, and reporting solutions that replace manual Excel\-based processes.
- Deploy modern BI and visualization tooling (e.g., Power BI, Tableau, Looker) to deliver self\-service analytics for finance and business partners.
- Evaluate and implement EPM / planning platforms (e.g., Anaplan, Pigment, Workday Adaptive, OneStream) and integrations as part of the transformation roadmap.
- Apply continuous improvement and agile delivery practices to drive measurable cycle\-time, accuracy, and productivity gains.
Data Architecture \& Governance
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- Lead the design and ongoing stewardship of the FP\&A data architecture, including financial data marts, dimensional models, hierarchies, and master data (e.g., chart of accounts, cost centers, legal entities, products).
- Establish data governance, quality, and controls frameworks specific to FP\&A, in partnership with enterprise data governance functions.
- Define standards for metric definitions, source\-of\-truth datasets, and certified reports to drive consistency across the organization.
- Ensure FP\&A data solutions adhere to security, privacy, SOX, and audit requirements.
Stakeholder Partnership \& Influence
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- Act as a trusted advisor to FP\&A leadership and business partners, translating business questions into data and reporting solutions.
- Proactively identify gaps, risks, and opportunities in the data and reporting environment — surface them, recommend solutions, and drive them to resolution without waiting to be asked.
- Partner cross\-functionally with IT, Data Engineering, and external vendors to deliver on transformation initiatives, including requirements definition, solution design, and implementation oversight.
- Drive change management and enablement, including training, documentation, and adoption tracking for new tools and processes.
- Influence outcomes through expertise and collaboration rather than direct people management.
Qualifications:
- Bachelor's degree in a relevant field (minimum requirement; advanced degrees are not required).
- 8\+ years of progressive, hands\-on experience that combines a strong understanding of financials with a data transformation background, including business intelligence, data analysis, and process transformation experience.
- Demonstrated experience leading data transformation, finance modernization, or reporting automation initiatives end\-to\-end as an individual contributor.
- Solid understanding of financial concepts and FP\&A processes (budgeting, forecasting, long\-range planning, management reporting, and variance analysis) — enough to partner credibly with finance stakeholders and translate their needs into data solutions.
- Strong data analysis skills — ability to independently explore, validate, and interpret large financial datasets, surface trends and anomalies, and turn raw data into clear, decision\-ready insights.
- Proven track record delivering business intelligence solutions and modernizing reporting environments (e.g., Power BI, Tableau, Looker), including advanced Excel.
- Process transformation experience — identifying inefficiencies, redesigning workflows, and implementing automation to drive measurable improvements in cycle time, accuracy, and scalability.
- Hands\-on expertise with modern data platforms and tools such as SQL, data warehouses (e.g., Snowflake, BigQuery, Redshift, Databricks), and ETL/ELT tools (e.g., dbt, Fivetran, Airflow).
- Hands\-on experience owning or administering an EPM / forecasting / planning tool (e.g., Anaplan, Pigment, Workday Adaptive, OneStream, Hyperion), including model design, integrations, and end\-user support.
- Working knowledge of ERP systems (e.g., Oracle, SAP, NetSuite, Workday).
- Track record of partnering with IT and Data Engineering on data architecture, integration, and governance.
- Excellent communication skills, with the ability to translate complex data concepts for finance and executive audiences.
Preferred Qualifications
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- Experience in a public company or scaling high\-growth environment with SOX compliance requirements.
- Familiarity with Python or other scripting languages for data manipulation and automation.
- Experience with AI / ML use cases in Finance (e.g., forecasting, anomaly detection, narrative generation).
- Prior experience in management consulting, Big 4 advisory, or a Finance Transformation Center of Excellence.
Key Competencies
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- Proactive self\-starter — anticipates needs, identifies issues and opportunities ahead of stakeholders, and drives them to resolution without waiting for direction.
- Strong analytical mindset — naturally curious about data, comfortable digging into details to validate accuracy and uncover insights.
- Strategic thinking with strong execution discipline — able to set vision and ship results.
- Bias for action and continuous improvement; comfortable navigating ambiguity.
- Strong systems thinking; sees data, process, and technology as one operating model.
- Influential communicator and collaborator across Finance, IT, and the business — able to drive outcomes through expertise and partnership rather than direct authority.
- Highly self\-directed; thrives owning end\-to\-end work as an individual contributor.
The salary range posted below refers only to positions that will be physically based in New York, NY. As with all roles, Medidata sets ranges based on a number of factors including function, level, candidate expertise and experience, and geographic location. Pay ranges for candidates in locations other than New York, NY may differ based on the local market data in that region. The base salary pay range for this position is $135,000 to $180,000\.
Base pay is one part of the Total Rewards that Medidata provides to compensate and recognize employees for their work. Most sales positions are eligible for a commission on the terms of applicable plan documents, and many of Medidata's non\-sales positions are eligible for annual bonuses. Medidata believes that benefits should connect you to the support you need when it matters most and provides best\-in\-class benefits, including medical, dental, life and disability insurance; 401(k) matching; flexible paid time off; and 10 paid holidays per year.
Note: Please be on the lookout for job scams. Medidata recruiters will never ask applicants for monetary compensation, credit card, or banking details.
Equal Employment Opportunity:
In order to provide equal employment and advancement opportunities to all individuals, employment decisions at Medidata are based on merit, qualifications and abilities. Medidata is committed to a policy of non\-discrimination and equal opportunity for all employees and qualified applicants without regard to race, color, religion, gender, sex (including pregnancy, childbirth or medical or common conditions related to pregnancy or childbirth), sexual orientation, gender identity, gender expression, marital status, familial status, national origin, ancestry, age, disability, veteran status, military service, application for military service, genetic information, receipt of free medical care, or any other characteristic protected under applicable law. Medidata will make reasonable accommodations for qualified individuals with known disabilities, in accordance with applicable law.
*Applications will be accepted on an ongoing basis until the position is filled.*
\#LI\-TC1
\#LI\-Hybrid
### Inclusion Statement
As a game\-changer in sustainable technology and innovation, Medidata, a Dassault Systèmes company, is striving to build more inclusive teams across the globe. We believe that our people are our number one asset and we want all employees to feel empowered to bring their whole selves to work every day. It is our goal that our people feel a sense of pride and a passion for belonging. As a company leading change, it's our responsibility to foster opportunities for all people to participate in a harmonized Workforce of the Future.
Inclusion statement
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In order to provide equal employment and advancement opportunities to all individuals, employment decisions at 3DS are based on merit, qualifications and abilities. 3DS is committed to a policy of non\-discrimination and equal opportunity for all employees and qualified applicants without regard to race, color, religion, gender, sex (including pregnancy, childbirth or medical or common conditions related to pregnancy or childbirth), sexual orientation, gender identity, gender expression, marital status, familial status, national origin, ancestry, age (40 and above), disability, veteran status, military service, application for military service, genetic information, receipt of free medical care, or any other characteristic protected under applicable law. 3DS will make reasonable accommodations for qualified individuals with known disabilities, in accordance with applicable law. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable state laws and local ordinances. We are committed to fair employment practices and will evaluate all candidates based on their qualifications, regardless of past arrest or conviction history.
Salary Pay Transparency
Compensation for the role will be commensurate with experience. The total expected compensation range will be between $135000 and $180000, representing the base salary (or annualized salary based on estimated hourly compensation) and target bonus.
Salary Context
This $135K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Medidata Solutions, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($157K) sits 13% below the category median. Disclosed range: $135K to $180K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Medidata Solutions AI Hiring
Medidata Solutions has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in New York, NY, US. Compensation range: $128K - $180K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national median.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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