Interested in this Data Scientist role at Fractal Analytics?
Apply Now →Skills & Technologies
About This Role
It's fun to work in a company where people truly BELIEVE in what they are doing!
*We're committed to bringing passion and customer focus to the business.*
Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets; an ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner.
Please visit Fractal \| Intelligence for Imagination for more information about Fractal.
Position Overview:
Fractal Analytics is seeking a GenAI Data Scientist with hands\-on expertise in building production\-grade Large Language Model (LLM)\-powered applications such as agentic chatbots, semantic search engines, and contextual assistants. The ideal candidate will be deeply technical with a strong foundation in LLM architecture and fine\-tuning, strong understanding of Foundation Model capabilities, RAG design and performance evaluation frameworks. This role is central to driving the development of innovative generative AI experiences that empower users and transform enterprise decision\-making.
Key Responsibilities:
- LLM\-based Solution Development
- Design and develop LLM\-powered applications such as agentic chatbots, smart search, contextual recommendation systems, and document summarizers.
- Fine\-tune open\-source and proprietary foundation models (e.g., GPT, LLaMA, Claude) for domain\-specific tasks using best practices.
- Implement Retrieval\-Augmented Generation (RAG) frameworks for enterprise\-grade knowledge access.
- Integrate AI assistants with internal systems and APIs for multi\-step reasoning and tool usage.
- Technical Innovation \& Applied Research
- Evaluate emerging GenAI tools and frameworks and incorporate them into scalable architectures.
- Experiment with techniques like few\-shot learning, prompt tuning, instruction tuning, and tool use (e.g., LangChain, LlamaIndex).
- Contribute to IP and internal assets for reusable components and accelerators.
- Model Evaluation \& Governance
- Design robust benchmarking and evaluation pipelines for LLM outputs (e.g., factual accuracy, hallucination rates, usefulness).
- Ensure responsible AI practices—bias detection, safety constraints, and interpretability.
- Collaborate with AI Governance and MLOps teams to ensure scalable and auditable deployments.
- Collaboration \& Solution Delivery
- Work closely with solution architects, UI engineers, and domain experts to define end\-to\-end product flows.
- Translate business requirements into technical blueprints and iterate through prototypes to production.
- Provide technical mentorship to junior data scientists and AI engineers.
Required Qualifications \& Skills:
- Generative AI Expertise
- Strong experience working with LLMs (e.g., GPT\-4, LLaMA, Claude, PaLM) and frameworks such as Hugging Face, LangChain, LlamaIndex, or Haystack.
- Experience implementing agent\-based architectures for autonomous task execution.
- Solid grounding in NLP techniques including embeddings, vector databases, text classification, summarization, and QA systems.
- Engineering \& Deployment
- Proficiency in Python and ML libraries like PyTorch, TensorFlow, scikit\-learn.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Familiarity with MLOps practices and tools for model deployment and monitoring.
- Business Acumen \& Communication
- Ability to understand user pain points and propose intuitive AI solutions.
- Strong problem\-solving and communication skills to work across technical and business stakeholders.
- Proven track record of delivering scalable GenAI solutions in enterprise environments.
Preferred Qualifications:
- Master’s/PhD in Computer Science, AI, Data Science, or a related field.
- Experience deploying GenAI solutions in a B2B enterprise or consulting environment.
- Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate, Chroma) and hybrid search strategies.
Why Join Fractal Analytics?
Opportunity to work on impactful projects and develop scalable solutions for our clients which are some of the world’s most valuable companies
Learn and grow with a diverse, high\-performing team building cutting edge AI, ML and Engineering solutions
Competitive compensation and benefits, with opportunities for professional growth and development.
Pay:
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Fractal, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is: $150,000 to $170,000 Yearly. In addition, you may be eligible for a discretionary bonus for the current performance period.
Benefits:
As a full\-time employee of the company or as an hourly employee working more than 30 hours per week, you will be eligible to participate in the health, dental, vision, life insurance, and disability plan in accordance with the plan documents, which may be amended from time to time. You will be eligible for benefits on the first day of employment with the Company. In addition, you are eligible to participate in the Company 401(k) Plan after 30 days of employment, in accordance with the applicable plan terms. The Company provides 11 paid holidays and 12 weeks of Parental Leave. We also follow a “free time” PTO policy, allowing you the flexibility to take the time needed for either sick time or vacation.
*Fractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.*
If you like wild growth and working with happy, enthusiastic over\-achievers, you'll enjoy your career with us!
### Hiring Related Queries
India: HiringsupportIndia@fractal.ai
Outside India: HiringsupportROW@fractal.ai
This inbox does not process resume submissions. All applications must be made through posted job openings
Not the right fit? Let us know you're interested in a future opportunity by clicking *Introduce Yourself* in the top\-right corner of the page or create an account to set up email alerts as new job postings become available that meet your interest!
Salary Context
This $150K-$170K range is above the median 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 Fractal Analytics, 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 ($160K) sits 17% below the category median. Disclosed range: $150K to $170K.
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.
Fractal Analytics AI Hiring
Fractal Analytics has 4 open AI roles right now. They're hiring across Data Scientist, MLOps Engineer, AI/ML Engineer. Positions span CA, US, New York, NY, US. Compensation range: $140K - $205K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.