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About This Role
At Lantern, our company culture stands as the bedrock of our success and a source of pride for our teams. We firmly believe that a culture founded on trust forms the basis for enduring relationships with clients, colleagues, and partners.
Within this culture, we nurture an environment of respect, inclusion, and belonging, fostering collaboration among inspired teams. We prioritize the well-being of our colleagues, the success of our clients, and our positive impact on society.
Embracing a growth mindset where curiosity thrives, we celebrate excellence and value individuals who inspire and mentor others, elevating the collective. Our driving force lies in personal and business growth. We go above and beyond to surprise and delight our clients, delivering tangible business value. In facing challenges, we make tough choices and solve complex problems to positively influence our clients, their customers, and the world at large.
As a Microsoft services partner, we hold ourselves to the highest standards of technical excellence. This commitment to quality is evident not only in our work but also in how we support and empower our employees. At Lantern, our culture mirrors our core values and unwavering dedication to realizing our purpose and vision, making it a dynamic and fulfilling workplace. Together, we transcend the ordinary and achieve extraordinary results.
Lantern is seeking a hands-on, client-facing Senior Data Scientist to design, develop, and operationalize advanced analytics, machine learning, generative AI, and agentic AI solutions using Azure Databricks and the Microsoft data and AI ecosystem. You will partner with client stakeholders, data engineers, architects, and application teams to translate business problems into secure, governed, scalable, and explainable AI solutions - from experimentation and feature engineering through deployment, monitoring, and measurable adoption. Key Responsibilities
- Lead discovery sessions to define analytical problems, success metrics, data requirements, experimentation plans, and production roadmaps.
- Explore and prepare structured and unstructured data; perform feature engineering, statistical analysis, and model selection using Python, SQL, Spark, and Databricks notebooks.
- Build, evaluate, and tune predictive, forecasting, optimization, natural language processing, computer vision, generative AI, and agentic AI solutions.
- Use Azure Databricks capabilities such as Delta Lake, Unity Catalog, MLflow, Feature Engineering, Model Serving, Mosaic AI, and vector search to create governed, production-ready solutions.
- Integrate solutions with Microsoft Azure AI Foundry, Azure OpenAI, Microsoft Fabric, OneLake, Power BI, and Azure services as appropriate.
- Implement MLOps and LLMOps practices including source control, automated testing, CI/CD, model registration, deployment, monitoring, drift detection, responsible AI, and cost optimization.
- Communicate findings and model behavior through clear visualizations, executive-ready narratives, demonstrations, and technical documentation.
- Own assigned workstreams, manage risks and dependencies, and collaborate with client teams to drive adoption and measurable business outcomes.
- Contribute to proposals, reusable accelerators, technical standards, peer reviews, mentoring, and the growth of Lantern's Databricks and Microsoft AI practices.
Skills, Knowledge and Expertise
- 5+ years of experience developing and delivering data science, machine learning, or AI solutions, preferably in consulting or other client-facing environments.
- Strong hands-on experience with Azure Databricks for data preparation, distributed model development, experiment tracking, model lifecycle management, and production deployment.
- Advanced proficiency in Python and SQL, with practical experience in PySpark and common data science frameworks such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
- Strong foundation in statistics, supervised and unsupervised learning, feature engineering, model evaluation, explainability, and experimental design.
- Experience developing generative AI solutions using large language models, retrieval-augmented generation, embeddings, vector databases, prompt engineering, evaluation, and agentic patterns.
- Working knowledge of Microsoft Azure AI Foundry, Azure OpenAI, Microsoft Fabric, OneLake, Power BI, and the broader Azure data and AI ecosystem.
- Experience with MLOps or LLMOps, CI/CD, model serving and monitoring, data governance, Unity Catalog, security, responsible AI, performance, and cost optimization.
- Strong consulting, communication, visualization, documentation, problem-solving, and stakeholder-management skills, with the ability to explain complex models to technical and executive audiences.
Preferred Certifications and Credentials
- Databricks Certified Machine Learning Professional or Machine Learning Associate
- Databricks Certified Generative AI Engineer Associate
- Databricks Certified Data Engineer Associate or Professional
- Microsoft Certified: Azure Data Scientist Associate (DP-100)
- Microsoft Certified: Azure AI Engineer Associate (AI-102)
- Microsoft Certified: Fabric Data Engineer Associate (DP-700) or Fabric Analytics Engineer Associate (DP-600)
- Databricks Solutions Architect Champion is highly preferred and considered a strong differentiator
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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Lantern, 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 1,406 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 $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Lantern AI Hiring
Lantern has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Dallas, TX, US, Dallas, TX, New York, NY, US. Compensation range: $275K - $275K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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