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About This Role
Overview:
The Cotiviti Retail division are seeking a Senior Insights Analyst to work closely with our Retail operations and R\&D teams. In this role, the analyst will design and develop reports and dashboards, provide analysis of complex data sets and provide actionable insights and strategic recommendations that drive business growth and optimize performance.
This is a key role in guiding the data analysis process and collaborating with stakeholders to identify opportunities and implement data\-driven solutions. This position would act as a subject matter expert to the team.
We are looking for a special individual who is not only technically sound but who also has experience in retail and financial systems. This individual should also be curious and interested in learning our unique and results\-oriented business.
Responsibilities:
Report Design and Development
- Utilize BI tools to enhance and support existing reporting/data warehouse environment, including use of SQL Management Studio, SSRS, SSIS, PowerShell and Power BI and Tableau.
- Support the Retail/Cotiviti management and other stakeholders in evaluating, defining, standardizing, and reporting on key KPI's.
- Provide expertise for the design and requirements for both executive and client facing reports, scorecards, and dashboards.
- Design and implement data visualization dashboards and reports to effectively communicate insights to stakeholders at all levels of the organization.
- Manage development lifecycle of report generation (development, test/QA, UAT, bug fix/retest cycles).
Data Analysis and Interpretation:
- Utilize advanced analytical techniques to uncover trends, patterns, and correlations within large and diverse data sets.
- Interpret analysis results and translate them into actionable recommendations for business stakeholders.
- Interpret data to identify business trends, conduct root cause analysis of business problems/bottlenecks, or suggest areas for process improvement.
- Conduct deep\-dive analysis and root cause investigations to address complex business challenges and opportunities.
- Lead the development and execution of data analysis projects, from data collection to insights generation and presentation.
- Generate actionable insights by synthesizing data analysis findings with industry knowledge and business context.
Market Research and Competitive Analysis:
- Conduct market research to identify industry trends, consumer behavior, and competitive landscape.
- Perform competitive analysis to benchmark against industry peers and identify opportunities for differentiation.
- Stay current with emerging trends and best practices in data analysis, business intelligence, and industry\-specific knowledge.
- Lead the evaluation and adoption of new tools, technologies, and methodologies to enhance the efficiency and effectiveness of the analytics function.
Cross\-functional Collaboration:
- Collaborate with cross\-functional teams, including marketing, sales, product development, and finance, to understand business objectives and priorities.
- Work closely with business stakeholders to define analytical requirements and tailor insights to meet their needs.
- Collaborate with cross\-functional teams to understand business objectives, identify key performance indicators (KPIs), and prioritize analytical initiatives.
- Present findings, recommendations, and insights to senior leadership and key stakeholders in a clear, concise, and compelling manner.
- Solicit requirements and Service Level Agreements (SLAs) from key stakeholders for ad hoc and/or complex reporting requests.
Data Governance and Compliance:
- Adhere to data governance policies and ensure compliance with data privacy regulations and industry standards.
- Safeguard sensitive data and ensure appropriate access controls are in place to protect data integrity and confidentiality.
- Complete all responsibilities as outlined in the annual performance review and/or goal setting.
- Complete all special projects and other duties as assigned.
- Must be able to perform duties with or without reasonable accommodation.
This job description is intended to describe the general nature and level of work being performed and is not to be construed as an exhaustive list of responsibilities, duties and skills required. This job description does not constitute an employment agreement and is subject to change as the needs of Cotiviti and the requirements of the job change.*
Qualifications:
- Bachelor’s degree in business administration, Economics, Statistics, Mathematics, Computer Science, or related field is required, or an equivalent combination of education and/or relevant experience.
- Minimum of 5 years of related experience.
- Proven experience in data analysis, business intelligence, or a related field, with a minimum of 5 years of relevant experience.
- Expertise in data manipulation, statistical analysis, and data visualization techniques, with proficiency in tools such as SQL, Python, R, Tableau, or Power BI.
- Demonstrated experience creating, debugging, and modifying complex stored procedures and views, as well as querying and updating data using SQL. Previous experience with SSIS is a plus.
- Experience with data governance, data quality management, and data security best practices.
- Strong leadership and mentoring skills, with the ability to effectively guide and support a team of analysts.
- Excellent communication and presentation skills, with the ability to convey complex technical concepts to non\-technical audiences.
- Demonstrated ability to manage multiple projects simultaneously, prioritize tasks, and meet deadlines in a fast\-paced environment.
- Strategic thinking and problem\-solving skills, with a focus on driving actionable insights and recommendations that align with business objectives.
- Experience working with large and diverse data sets, including structured and unstructured data sources.
- Knowledge of advanced analytical techniques such as machine learning, predictive modeling, and optimization algorithms is preferred.
- Strong business acumen and understanding of key business processes, drivers, and metrics within the organization or industry.
- Certification in relevant areas such as data analysis, business intelligence, or project management is a plus.
- Experience in a leadership or managerial role is advantageous.
- Understands and embodies Cotiviti Core Values, Strategic Pillars, and Operations Disciplines to achieve successful performance in completing assigned responsibilities and interactions with the Organization both internally and externally.
Mental Requirements:* Critical Thinking: Ability to think critically and evaluate information objectively, considering different perspectives and potential implications before drawing conclusions or making recommendations.
- Attention to Detail: must have a keen eye for detail to ensure accuracy in data analysis, interpretation, and reporting.
- Quantitative Aptitude: Strong numerical skills are essential for conducting quantitative analysis, working with statistical methods and models, and manipulating data using mathematical operations.
- Data Interpretation: skilled in interpreting data visualizations, charts, graphs, and other forms of data presentation to extract meaningful insights and communicate findings effectively.
- Communication Skills: Effective communication skills are crucial for conveying complex technical concepts and insights to non\-technical stakeholders clearly and understandably through written reports, presentations, and verbal discussions.
- Curiosity and Learning Agility: A strong desire to learn and explore new methodologies, techniques, and tools in the field of data analysis and insights generation is essential for staying current with industry trends and best practices.
- Resilience: The ability to handle pressure, adapt to changing priorities, and overcome setbacks is important in a fast\-paced and sometimes ambiguous analytical environment.
- Ethical and Integrity: Upholding ethical standards and maintaining integrity in handling sensitive data and information is paramount for building trust and credibility in the insights provided.
Physical Requirements and Working Conditions:* Remaining in a stationary position, often standing or sitting for prolonged periods.
- Repeating motions that may include the wrists, hands, and/or fingers.
- Must be able to provide high\-speed internet access/connectivity and office setup and maintenance.
- Must be able to provide a dedicated, secure work area.
- Exposure to moderate noise intensity is expected.
- No adverse environmental conditions are expected.
*Base compensation ranges from $105,000 to $140,000 per year. Specific offers are determined by various factors, such as experience, education, skills, certifications, and other business needs. This* *role is based remotely, and all interviews will be conducted virtually.* *Cotiviti offers team members a competitive benefits package to address a wide range of personal and family needs, including medical, dental, vision, disability, and life insurance coverage, 401(k) savings plans, paid family leave, 9 paid holidays per year, and 17\-27 days of Paid Time Off (PTO) per year, depending on specific level and length of service with Cotiviti. For information about our benefits package, please refer to our* *Careers page.*
Date of posting: 03/11/2025
Applications are assessed on a rolling basis. We anticipate that the application window will close on 06/09/2026, but the application window may change depending on the volume of applications received or close immediately if a qualified candidate is selected.
\#LI\-Remote
\#LI\-RA1
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Salary Context
This $105K-$140K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Cotiviti, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($122K) sits 27% below the category median. Disclosed range: $105K to $140K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Cotiviti AI Hiring
Cotiviti has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Safety, AI Software Engineer. Based in Remote, US. Compensation range: $124K - $180K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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.
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