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About This Role
About Constellation:
Constellation is a cutting\-edge AI solution that empowers highly regulated and complex industries with the insights and content they need to fuel their business. Specializing in industries such as healthcare, automotive, insurance, and finance, our powerful data/AI insights tools inform the creation of compliant content at scale. We enable our customers to harness their data and streamline the creation of localized, personalized content. A global, NYC\-based company, Constellation has been revolutionizing marketing technology and data intelligence in order to drive exponential growth since its founding in 2016\.
Constellation was named the 65th Fastest\-Growing Private Company in America, the 10th Fastest\-Growing Women\-Owned Private Company, and the 7th Fastest\-Growing Marketing \& Advertising Company by Inc 500\. In 2022, our platform won the Digiday Technology Award for Best Marketing Automation Platform.
About The Role
We're looking for a Senior Data Scientist to take a leading role in building the AI that powers Constellation. You'll ship production models—generative AI, autonomous agents, and predictive models for churn, LTV, and pricing— while mentoring a growing team and shaping our technical standards. If you want your work to reach real users in regulated industries like automotive and life sciences (not sit in a notebook), this is that role.
What You'll Do
- Lead AI/ML Initiatives: Architect, prototype, and productionize ML models, including generative AI, recommendation systems, and domain\-specific agents tailored to marketing, regulatory compliance, and creative workflows.
- Drive Agentic AI Capabilities: Build agent\-based systems that reason, retrieve, validate, and act autonomously, supporting prompt chaining, tool usage, and evaluation feedback loops.
- Mentor \& Scale the Team: Coach junior and mid\-level data scientists and engineers, review code, drive standards, and help shape a culture of high performance, curiosity, and inclusivity.
- Collaborate Cross\-Functionally: Work closely with Product, Engineering, Design, and GTM teams to integrate intelligent systems into customer\-facing features and internal tooling.
- Model Business\-Critical Outcomes: Develop models for customer LTV, churn, content efficacy, user engagement, and pricing optimizations, leveraging both structured and unstructured data.
- Own the ML Stack: Contribute to architecture decisions on model lifecycle management, pipelines, monitoring, and experiment tracking using modern tooling like MLflow, SageMaker, or Vertex AI.
- Drive Applied Research: Stay on top of advances in transformers, large language models, retrieval\-augmented generation, and probabilistic programming—and bring these innovations to production.
What We're Looking For
- 8\+ years of experience in data science, ML engineering, or applied AI, with a track record of shipping models in production.
- Deep experience with Python (Pandas, NumPy, Scikit\-learn) and modern ML/DL libraries (PyTorch, TensorFlow, Hugging Face, LangChain).
- Strong command of SQL and data modeling in cloud warehouses (e.g., Snowflake, BigQuery, Redshift).
- Experience building systems using AWS (Lambda, SageMaker, EC2, Batch, S3\) or similar cloud platforms.
- Solid understanding of ETL/data pipelines and orchestration tools (e.g., Airflow).
- Experience with agent\-based architectures, LLMs, and real\-world generative AI applications (NLP, content synthesis, summarization, retrieval).
- Familiarity with automotive or life sciences data domains is a strong plus—whether in marketing analytics, medical devices, vehicle inventory intelligence, or regulatory content.
- Outstanding communication and mentorship skills, with a collaborative mindset and a passion for helping others grow.
Bonus Experience
- Experience with probabilistic modeling (e.g., PyMC3, Stan).
- Familiarity with graph databases (e.g., Neo4j) and modeling network effects.
- Prior exposure to recommendation engines, survival models, or multi\-touch attribution.
- Experience with deploying and evaluating LLMs, retrieval pipelines, or autonomous AI agents in production.
- Exposure to Looker, Tableau, or similar BI platforms.
Why Constellation?
- Work on real\-world AI systems that impact regulated, high\-stakes industries.
- Collaborate with a team of ambitious, principled builders who care about quality, ethics, and impact.
- Join a design\-forward organization with strong product leadership and a rapidly growing client base.
- Enjoy flexibility, autonomy, and opportunities to lead across the stack.
Compensation
NY Pay Range: $170,000 – $200,000
Final package commensurate with experience. Includes base salary, equity options, and benefits.
*Constellation is committed to diversity, inclusion, and equal opportunity. If you require accommodation during the interview process, please let us know.*
\#LI\-Hybrid
Salary Context
This $170K-$200K range is above the median for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Constellation Software, Inc., 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 $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($185K) sits 7% below the category median. Disclosed range: $170K to $200K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Constellation Software, Inc. AI Hiring
Constellation Software, Inc. has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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