Senior Data Scientist

Constellation Software, Inc.•New York, NY

1d•$170,000 - $200,000•Hybrid

About The Position

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.

Requirements

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).
Outstanding communication and mentorship skills, with a collaborative mindset and a passion for helping others grow.

Nice To Haves

Familiarity with automotive or life sciences data domains is a strong plus—whether in marketing analytics, medical devices, vehicle inventory intelligence, or regulatory content.
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.

Responsibilities

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.