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About This Role
Senior Data Scientist / ML Engineer (Generative AI)
Experience: 5–10 years
Location: Onsite / Offshore (Flexible)
Primary Objective
We are seeking a Senior Data Scientist / ML Engineer specializing in Generative AI to design, evaluate, optimize, and productionize AI/ML solutions for enterprise applications—including RAG systems, AI agents, intelligent automation, and model evaluation platforms.
The role focuses on improving AI accuracy and retrieval quality, reducing hallucinations, benchmarking LLMs, and building reliable solutions for enterprise\-scale deployments.
Success looks like: measurable gains in retrieval/answer quality, robust evaluation frameworks in production, and clear collaboration with AI engineering to ship governed, reliable GenAI systems.
Key Responsibilities
Primary
- Design and develop machine learning and Generative AI solutions.
- Build and optimize RAG pipelines, retrieval strategies, embeddings, and semantic search.
- Evaluate and benchmark LLMs for accuracy, performance, and reliability.
- Develop AI evaluation frameworks for hallucination detection, accuracy measurement, bias/toxicity detection, and ground\-truth validation.
- Optimize prompts, models, and retrieval workflows.
- Collaborate with AI engineering teams to deploy models into production.
Also expected
- Create training, validation, and testing datasets.
- Perform model benchmarking, A/B testing, and performance analysis.
- Fine\-tune foundation models when required.
- Implement model monitoring, observability, and ongoing evaluation processes.
Must\-Have Experience \& Skills
- 5–10 years of experience in data science, machine learning, or related applied ML roles.
- Strong Python programming skills, with Pandas, NumPy, and Scikit\-learn.
- Strong foundation in supervised/unsupervised learning, statistical modeling, feature engineering, and model evaluation techniques.
- Hands\-on Generative AI experience with LLM evaluation, prompt engineering, RAG architectures, embedding models, fine\-tuning approaches, and agent evaluation frameworks.
- Experience with PyTorch and/or TensorFlow.
- Exposure to OpenAI models, Claude, Gemini, and/or open\-source LLMs.
- Experience with vector databases, semantic search, and retrieval optimization.
- Experience delivering or supporting production AI/ML solutions in enterprise environments.
- Experience working with distributed onshore/offshore teams.
Preferred Skills
- Databricks, MLflow, and Spark.
- GraphRAG and Knowledge Graphs; exposure to Neo4j.
- Responsible AI, Explainable AI, and AI governance.
- Banking or Financial Services domain experience.
- Familiarity with Azure AI Foundry, AWS Bedrock, Kubernetes, and AI observability platforms.
Soft Skills
- Clear communication with engineering and business stakeholders.
- Ability to translate evaluation results into actionable model/product decisions.
- Comfortable owning quality metrics and trade\-offs (accuracy, latency, cost, risk) in a delivery setting.
Compensation, Benefits and Duration
Minimum Compensation: USD 60,000
Maximum Compensation: USD 210,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is available for independent contractors
No applications will be considered if received more than 120 days after the date of this post.
Salary Context
This $60K-$210K range is below the median for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Photon, 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 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($135K) sits 30% below the category median. Disclosed range: $60K to $210K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Photon AI Hiring
Photon has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span US, New York, NY, US. Compensation range: $168K - $224K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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