Position Details
Data Scientist Apply
Title: Data Scientist
Location: NYC, NY or Remote
Type: Contract
About the Role
We are seeking an experienced Data Scientist to join our AI &; Data Science team, with a
strong emphasis on modern data engineering and platform-based development. This role
focuses on building scalable, production-grade data and machine learning pipelines that
power analytics, predictive models, and AI-driven insights across the firm, including work
within our Databricks environment.
strong emphasis on modern data engineering and platform-based development. This role
focuses on building scalable, production-grade data and machine learning pipelines that
power analytics, predictive models, and AI-driven insights across the firm, including work
within our Databricks environment.
In addition, this role plays a key part in the development, enhancement, and
operationalization of Generative AI and Large Language Model (LLM) powered tools that
support executive recruiting and leadership advisory workflows.
While advanced analytical thinking is essential, this position is highly execution-oriented.
You will spend the majority of your time designing data architectures, developing robust
Python-based pipelines, operationalizing models and GenAI systems, and partnering closely
with engineering teams to deploy and maintain solutions in production.
This role also requires strong foundational data analysis and statistical reasoning, with the
ability to translate ambiguous leadership and talent questions into analytical frameworks,
insights, and recommendations that are credible and actionable for consultants and executive
clients.
Key Responsibilities
Data Engineering and Machine Learning
Design, build, and maintain scalable data pipelines using Azure systems, Databricks, and Python to support analytics, machine learning, and AI use cases
Develop and operationalize machine learning models, ensuring reproducibility, performance, and reliability in production environments
Partner with data engineers and platform teams to optimize data ingestion,transformation, and storage across enterprise data sources
Write high-quality, production-ready Python code for feature engineering, model pipelines, and data validation
Establish monitoring and observability for data and ML pipelines, including data quality checks and model performance metrics
Contribute to shared libraries, standards, and best practices for data engineering and applied machine learning
Generative AI & LLM Enablement
Develop, enhance, and productionize LLM-powered applications such as candidate summarization, role profiling, market mapping, knowledge retrieval, and insight
generation
Improve existing GenAI tools through prompt engineering, evaluation frameworks, fine-tuning approaches, and retrieval-augmented generation (RAG) pipelines
Partner with engineering teams to integrate LLM services into enterprise platforms with appropriate controls for security, governance, and observability
Develop, enhance, and productionize LLM-powered applications such as candidate summarization, role profiling, market mapping, knowledge retrieval, and insight
generation
Improve existing GenAI tools through prompt engineering, evaluation frameworks, fine-tuning approaches, and retrieval-augmented generation (RAG) pipelines
Partner with engineering teams to integrate LLM services into enterprise platforms with appropriate controls for security, governance, and observability
Design and implement evaluation metrics for GenAI outputs, including accuracy, relevance, consistency, bias, and business usefulness
Monitor GenAI systems in production and iterate based on performance data and user feedback
Monitor GenAI systems in production and iterate based on performance data and user feedback
Applied Data Analysis for Executive Recruiting & Leadership Advisory
Conduct exploratory and structured data analysis to support executive search, leadership assessment, succession planning, and market intelligence
Translate leadership and talent questions into analytical hypotheses, metrics, and data requirements
Analyze candidate, role, and market data to identify patterns related to experience, career progression, leadership capabilities, and search outcomes
Partner with consultants, analytics teams, and business stakeholders to interpret results, validate assumptions, and communicate insights clearly
Ensure analytical outputs are statistically sound, context-aware, and suitable for executive-level decision-making
Conduct exploratory and structured data analysis to support executive search, leadership assessment, succession planning, and market intelligence
Translate leadership and talent questions into analytical hypotheses, metrics, and data requirements
Analyze candidate, role, and market data to identify patterns related to experience, career progression, leadership capabilities, and search outcomes
Partner with consultants, analytics teams, and business stakeholders to interpret results, validate assumptions, and communicate insights clearly
Ensure analytical outputs are statistically sound, context-aware, and suitable for executive-level decision-making
Required Qualifications
- 5+ years of experience in data science, data engineering, applied machine learning, or advanced analytics roles
- Advanced proficiency in Python, with demonstrated experience building production- grade data, ML, or GenAI pipelines
- Hands-on experience working with Generative AI or LLM-based systems, including prompt design, evaluation, and application integration
- Strong grounding in data analysis, statistics, and exploratory analysis
- Experience translating business or domain questions into analytical approaches and communicating insights to non-technical stakeholders
- Strong hands-on experience with Databricks (Spark, notebooks, jobs, Delta Lake) in a production environment
- Solid understanding of data modeling, ETL/ELT patterns, and distributed data processing
- Experience deploying and maintaining machine learning models in production environments
- Excellent communication skills, with the ability to explain complex technical concepts clearly
Preferred Skills
- Experience with MLflow, model registries, and experiment tracking
- Familiarity with version control and deployment workflows for data, ML, and GenAI solutions
- Experience with RAG architectures, vector databases, or semantic search
- Familiarity with GenAI evaluation techniques, including human-in-the-loop review
- Knowledge of SQL and data warehousing concepts
- Experience supporting talent analytics, people analytics, executive assessment, or professional services use cases
- Ability to design analyses and narratives appropriate for senior consultant and executive audiences
- Experience working in regulated or enterprise environments with an emphasis on reliability, governance, and trust
