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About This Role
Data Scientist – Machine Learning / GenAI
Job Title: Data Scientist – Machine Learning & AI Location: Remote / Hybrid – USA Duration: 12+ Months Contract Experience: 5-10 Years
Job Summary
We are seeking an experienced Data Scientist with strong hands-on experience in machine learning, statistical modeling, neural networks, and predictive analytics, along with some practical experience working with Generative AI and Large Language Models (LLMs). The candidate will work with software engineering, and business teams to develop, evaluate, and deploy data-driven machine learning solutions.
Required Skills & Experience
- 5+ years of experience in Data Science, Machine Learning, or Applied AI.
- Strong programming experience with Python and commonly used data science libraries.
- Hands-on experience developing and deploying machine learning models for classification, regression, clustering, forecasting, or recommendation use cases.
- Strong understanding of supervised and unsupervised learning, feature engineering, model selection, validation, and performance evaluation.
- Practical experience with neural networks and deep learning.
- Experience with frameworks such as PyTorch, TensorFlow, or Keras.
- Understanding of neural network architectures including CNNs, RNNs/LSTMs, Transformers, or similar architectures.
- Experience working with NLP, text analytics, or unstructured data is highly desirable.
- Some hands-on experience with Generative AI / LLMs, including prompt engineering, embeddings, vector search, RAG, or LLM-based applications.
- Familiarity with OpenAI, Azure OpenAI, AWS Bedrock, Google Vertex AI, or comparable GenAI platforms is a plus.
- Experience with SQL and working with structured and unstructured datasets.
- Experience using Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn, or equivalent tools.
- Understanding of model evaluation, hyperparameter tuning, cross-validation, and model explainability.
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Familiarity with ML pipelines, MLOps, model deployment, and monitoring.
- Ability to translate business problems into measurable data science and machine learning solutions.
Key Responsibilities
- Analyze large and complex datasets to identify patterns, trends, and business opportunities.
- Develop and optimize machine learning models for business and operational use cases.
- Design and implement neural-network and deep-learning solutions where appropriate.
- Perform feature engineering, data preprocessing, model training, and validation.
- Evaluate models using appropriate statistical and machine-learning metrics.
- Experiment with different algorithms and architectures to improve model performance.
- Work with data engineers to prepare reliable datasets and scalable data pipelines.
- Develop proof-of-concepts using LLMs and Generative AI for selected business use cases.
- Build or contribute to RAG, embeddings, vector-search, and prompt-engineering solutions.
- Collaborate with engineering teams to productionize machine-learning models.
- Monitor model performance and recommend improvements or retraining strategies.
- Present analytical findings, model results, and recommendations to technical and business stakeholders.
Preferred Qualifications
- Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
- Experience with Transformers, LLMs, NLP, RAG, embeddings, or vector databases.
- Experience with Databricks, Spark, Snowflake, AWS, Azure, or GCP.
- Familiarity with Docker, Kubernetes, MLflow, Airflow, or CI/CD.
- Experience taking machine-learning models from prototype to production.
- Experience working in enterprise environments such as financial services, retail, healthcare, technology, or manufacturing.
Technical Stack
Languages: Python, SQL Machine Learning: Scikit-learn, XGBoost, LightGBM Deep Learning: PyTorch, TensorFlow, Keras AI/GenAI: LLMs, Prompt Engineering, RAG, Embeddings, Vector Search, Transformers Data: Pandas, NumPy, Spark, Databricks, Snowflake Cloud: AWS / Azure / GCP MLOps: MLflow, Docker, Kubernetes, CI/CD Visualization: Power BI, Tableau, Matplotlib, Seaborn
Core requirement: The candidate should be genuinely hands-on with traditional machine learning and neural networks, with practical exposure to GenAI/LLMs
Work Location: Remote
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 11,520 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Solutions Sync, 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 $190,000 based on 1,564 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Solutions Sync AI Hiring
Solutions Sync has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 Safety roles lead at $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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