Data Science Quant Analyst
Howard-Sloan Search, New York, NY, United States
Our client Global Investment Management Firm
is seeking a 4 days onsite / 1 day remote Data Science & Analytics (DSA) – Analyst (Quant) – Associate full time role.
Location: Park Avenue, New York offices; 4 days onsite / 1 day remote.
Salary : $90–100K base / 10–15% bonus
Level : Analyst, Data Science & Analytics
Must currently reside in the NY metro area.
This role reports to : MD, DSA & Executive Director, AI & Business Analysis (AI Lead)
Working directly with : MDs, Bankers, AI Business Analysts, Data Scientists, Developers, Business Management, Product/Technology Teams across the global organization.
Key Responsibilities
Apply rigorous quantitative methods to client and business problems.
Build, test, refine models and analytical assets using structured and unstructured data.
Translate data into clear findings, recommendations, and decision support.
Contribute to internal AI and analytics capabilities that can scale across teams.
Quantitative Analysis & Modeling
Perform rigorous statistical analysis, exploratory data analysis, feature engineering, and model development across a range of client-facing and internal use cases.
Build and refine predictive, classification, segmentation, NLP, and other analytical models using structured and unstructured datasets.
Evaluate model performance, document assumptions, and support model validation and testing.
Use Python, SQL, and related tools to extract, clean, transform, and analyze data efficiently and accurately.
Develop repeatable analytical approaches and reusable code that improve quality, speed, and consistency.
Client Delivery Support
Support senior team members in delivering analytical workstreams tied to client mandates, strategic analyses, and business development initiatives.
Help frame business questions into analytical hypotheses, required data inputs, and model approaches.
Prepare analyses, visualizations, and outputs that can be translated into clear client-ready materials.
Work with bankers and internal stakeholders to refine requirements, validate findings, and improve usability of outputs.
Contribute to high-priority, time-sensitive analyses in support of live deal, strategic, and sector work.
AI & Internal Platform Contribution
Contribute to the development of internal AI and analytics assets, including reusable workflows, data pipelines, prompts, evaluation approaches, and model-enabled tools.
Support the testing and refinement of AI-enabled workflows tied to knowledge retrieval, summarization, classification, and productivity enhancement.
Help identify opportunities to reuse client-facing analytical patterns within the firm’s internal AI build.
Partner with engineering and business teams to move analytical solutions from prototype to practical usage.
Measurement, Governance, and Controls
Document methodologies, data sources, model logic, and outputs in a clear and auditable manner.
Support adherence to internal standards related to model governance, data quality, explainability, and information security.
Assist in defining success metrics for analytical solutions, including accuracy, time saved, quality lift, and business impact.
Help maintain disciplined testing, issue tracking, and version control across analytical work.
Ways of Working
Operate with strong attention to detail, sound judgment, and a high bar for analytical quality.
Collaborate effectively across data science, business analysis, technology, and business stakeholders.
Learn quickly, absorb context fast, and contribute across multiple workstreams at once.
Stay current on emerging techniques in machine learning, analytics, and AI relevant to financial services.
Education
Bachelor’s degree required, preferably in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, Finance, or a related quantitative field.
Master’s degree is a plus, but not required.
Experience
0–2 years of experience in data science, analytics, quantitative consulting, financial services, or a related role.
Internship or full-time experience in investment banking, financial services, consulting, or a similarly demanding environment is preferred.
Experience supporting client-facing analytical work is a plus.
Core Skills
Strong quantitative, statistical, and problem-solving capabilities.
Ability to structure ambiguous questions into analytical workplans.
Strong written and verbal communication skills, including the ability to explain technical findings to non-technical audiences.
High attention to detail and ability to deliver under tight timelines.
AI, Data, and Technical Skills
Experience with Python required; SQL required.
Familiarity with common data science libraries and workflows for analysis, modeling, and visualization.
Exposure to machine learning techniques such as regression, classification, clustering, time series, optimization, or NLP.
Familiarity with working across structured and unstructured datasets.
Exposure to LLMs, prompt design, retrieval workflows, or AI tooling is a plus.
Familiarity with Power BI, Tableau, or similar visualization tools is a plus.
Familiarity with Git, notebook-based development, and sound coding/documentation practices is preferred.
Comfortable operating in a fast-paced, high-expectation environment.
Strong team player with a practical, execution-oriented mindset.
Interested in applying quantitative methods to real commercial and client outcomes.
#J-18808-Ljbffr
is seeking a 4 days onsite / 1 day remote Data Science & Analytics (DSA) – Analyst (Quant) – Associate full time role.
Location: Park Avenue, New York offices; 4 days onsite / 1 day remote.
Salary : $90–100K base / 10–15% bonus
Level : Analyst, Data Science & Analytics
Must currently reside in the NY metro area.
This role reports to : MD, DSA & Executive Director, AI & Business Analysis (AI Lead)
Working directly with : MDs, Bankers, AI Business Analysts, Data Scientists, Developers, Business Management, Product/Technology Teams across the global organization.
Key Responsibilities
Apply rigorous quantitative methods to client and business problems.
Build, test, refine models and analytical assets using structured and unstructured data.
Translate data into clear findings, recommendations, and decision support.
Contribute to internal AI and analytics capabilities that can scale across teams.
Quantitative Analysis & Modeling
Perform rigorous statistical analysis, exploratory data analysis, feature engineering, and model development across a range of client-facing and internal use cases.
Build and refine predictive, classification, segmentation, NLP, and other analytical models using structured and unstructured datasets.
Evaluate model performance, document assumptions, and support model validation and testing.
Use Python, SQL, and related tools to extract, clean, transform, and analyze data efficiently and accurately.
Develop repeatable analytical approaches and reusable code that improve quality, speed, and consistency.
Client Delivery Support
Support senior team members in delivering analytical workstreams tied to client mandates, strategic analyses, and business development initiatives.
Help frame business questions into analytical hypotheses, required data inputs, and model approaches.
Prepare analyses, visualizations, and outputs that can be translated into clear client-ready materials.
Work with bankers and internal stakeholders to refine requirements, validate findings, and improve usability of outputs.
Contribute to high-priority, time-sensitive analyses in support of live deal, strategic, and sector work.
AI & Internal Platform Contribution
Contribute to the development of internal AI and analytics assets, including reusable workflows, data pipelines, prompts, evaluation approaches, and model-enabled tools.
Support the testing and refinement of AI-enabled workflows tied to knowledge retrieval, summarization, classification, and productivity enhancement.
Help identify opportunities to reuse client-facing analytical patterns within the firm’s internal AI build.
Partner with engineering and business teams to move analytical solutions from prototype to practical usage.
Measurement, Governance, and Controls
Document methodologies, data sources, model logic, and outputs in a clear and auditable manner.
Support adherence to internal standards related to model governance, data quality, explainability, and information security.
Assist in defining success metrics for analytical solutions, including accuracy, time saved, quality lift, and business impact.
Help maintain disciplined testing, issue tracking, and version control across analytical work.
Ways of Working
Operate with strong attention to detail, sound judgment, and a high bar for analytical quality.
Collaborate effectively across data science, business analysis, technology, and business stakeholders.
Learn quickly, absorb context fast, and contribute across multiple workstreams at once.
Stay current on emerging techniques in machine learning, analytics, and AI relevant to financial services.
Education
Bachelor’s degree required, preferably in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, Finance, or a related quantitative field.
Master’s degree is a plus, but not required.
Experience
0–2 years of experience in data science, analytics, quantitative consulting, financial services, or a related role.
Internship or full-time experience in investment banking, financial services, consulting, or a similarly demanding environment is preferred.
Experience supporting client-facing analytical work is a plus.
Core Skills
Strong quantitative, statistical, and problem-solving capabilities.
Ability to structure ambiguous questions into analytical workplans.
Strong written and verbal communication skills, including the ability to explain technical findings to non-technical audiences.
High attention to detail and ability to deliver under tight timelines.
AI, Data, and Technical Skills
Experience with Python required; SQL required.
Familiarity with common data science libraries and workflows for analysis, modeling, and visualization.
Exposure to machine learning techniques such as regression, classification, clustering, time series, optimization, or NLP.
Familiarity with working across structured and unstructured datasets.
Exposure to LLMs, prompt design, retrieval workflows, or AI tooling is a plus.
Familiarity with Power BI, Tableau, or similar visualization tools is a plus.
Familiarity with Git, notebook-based development, and sound coding/documentation practices is preferred.
Comfortable operating in a fast-paced, high-expectation environment.
Strong team player with a practical, execution-oriented mindset.
Interested in applying quantitative methods to real commercial and client outcomes.
#J-18808-Ljbffr