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About This Role
Overview:
As the nation’s leading provider of high\-quality home care services, we empower our clients to live independently, safely, and with dignity in their own homes. The home is more than a place – it’s the center of health, care coordination, and Meaningful Moments that transform lives.
We're looking for a curious, analytical, and technically driven Data Scientist to join our growing Analytics \& Data Science team. This role will help develop predictive models, automate reporting, build data pipelines, and support AI\-driven initiatives that improve operations and patient care across one of the nation's largest home care providers. Remote Flexibility. Weekly Pay. Exceptional Career. Join a mission\-driven team with an annual salary range of $85,000–$113,000\.
Our Benefits:
- Comprehensive medical, dental, and vision coverage
- 401(k) retirement plan
- Paid time off and holidays
- Employee assistance programs and wellness initiatives
- Flexible options to support a balanced life
Responsibilities:
What You'll Do:
- Formulate analysis plans and selection of appropriate methods/tools for analysis.
- Support AI initiatives including large language model (LLM) applications and automation workflows.
- Work with business stakeholders, BI Analysts and Data Engineers on defining and building data sets to be analyzed.
- Provide statistical analysis of HAH’s internal data as well as data from external partners, HIEs and data aggregators (e.g. ADTs, CCDs, ORUs, claims, etc.).
- Use statistical rigor, scientific methods, data mining techniques, machine\-learning algorithms and methodologies in analyzing data.
- Apply methods for big data analysis to reveal patterns, trends, and associations in large data sets in search for usable information.
- Maintain familiarity with relevant published research and trends to align business inquiries with new and established evidence from the literature.
- Assist with population health management including statistical analysis – such as descriptive, inferential, correlations, multi\-variate regression and predictive modeling.
- Explain complex technical and statistical concepts to business audience and effectively communicate results and findings through appropriate visualizations of data.
- Build effective relationships with all cross functional leaders.
- Participate in senior leadership meetings and explain analysis results and limitations and present data driven business insights.
- Provides thought leadership \& strategic thinking to translate business problem into analytical framework(s), and independently recommend actions and provide business insights.
- Contribute to the development and implementation of the Enterprise Analytics team.
- This description reflects assignment of essential functions, management may assign or reassign duties and responsibilities to this job at any time that are not listed above.
Qualifications:
What You'll Bring:
- Strong analytical, statistical, data modeling and data management skills
- Ability to effectively navigate highly matrixed environment and establish good working relationships and communication with key business stakeholders
- Critical thinking, problem\-solving, curiosity \- add value by conducting inquiring explorations to identify meaningful trends and associations that can drive clinical and business decisions
- Intermediate to Advanced in all Microsoft Office software, specifically, PowerBI, PowerPoint, Excel, and Visio
- Working knowledge of R, Python, SPSS and SQL
- Knowledge of data science, machine learning and predictive models
- Snowflake, SageMaker Canvas, SageMaker Studio experience a plus, GIT
Education and Experience:
- Bachelor's degree in data science, Statistics, Mathematics, Computer Science, Engineering, Economics, or related field required.
- Three (3\) or more years of Healthcare data analysis experience.
- One (1\) or more year of experience with data science, applied statistics, predictive modeling, or machine learning projects.
- Familiarity with using AI tools, prompt engineering
- Experience in outcomes research, claims and medical cost analysis.
- Familiarity with Medicaid, Medicare and Duals space.
- Knowledge of healthcare operations and data structures and the use of information system applications in the practicing healthcare environment.
- Knowledge of home care industry a plus.
- Understanding of medical coding systems (ICD\-10, CPT, HCPCS, DRG, etc.)
Physical Requirements:
- Sedentary – ability to remain in a stationary position for extended periods of time.
Travel Requirements:
- Monthly or quarterly travel required
*The above statements are intended to describe the general nature and level of work being performed by people assigned to this classification. They are not intended to be construed as an exhaustive list of all responsibilities, duties, and skills required of personnel so classified. The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions upon request.* *Help At Home is an Equal Employment Opportunity (EEO) employer and welcomes all qualified applicants. Applicants will receive fair and impartial consideration without regard to race, sex, color, religion, national origin, age, disability, veteran status, genetic data, or religion or other legally protected status.*
Pay Range: USD $85,000\.00 \- USD $113,000\.00 /Yr.
Salary Context
This $85K-$113K range is in the lower quartile for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Help at Home, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($99K) sits 49% below the category median. Disclosed range: $85K to $113K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Help at Home AI Hiring
Help at Home has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $113K - $113K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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