Application Guide

How to Apply for Data Scientist

at SESAMm

🏢 About SESAMm

SESAMm is a specialized AI analytics firm that processes over 20 billion articles to extract ESG, sentiment, and thematic insights for corporate decision-making. Unlike general data science companies, SESAMm focuses specifically on alternative data analysis for investment and corporate strategy, making it unique in applying NLP and machine learning to financial markets. Working here offers exposure to cutting-edge AI applications in finance with direct impact on investment decisions.

About This Role

This Data Scientist role at SESAMm involves developing analytical models to support private equity investment decisions, with a focus on feature engineering, statistical modeling, and machine learning using diverse data sources including financial statements, market data, and ESG signals. You'll design and maintain data pipelines for large-scale alternative data while collaborating with investment teams to identify high-impact use cases. The position directly impacts investment outcomes by transforming unstructured alternative data into actionable insights for private equity clients.

💡 A Day in the Life

A typical day involves developing and refining ML models for ESG signal extraction from news articles, collaborating with investment teams to identify analytical needs for specific due diligence projects, and maintaining data pipelines that integrate financial statements with alternative datasets. You'll spend time cleaning and preprocessing large-scale text data, conducting statistical analysis to validate model outputs, and preparing clear visualizations and explanations of findings for investment stakeholders.

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🎯 Who SESAMm Is Looking For

  • Has 3-5 years of hands-on data science experience with specific expertise in feature engineering, statistical modeling, and ML model evaluation using Python (Pandas, NumPy)
  • Possesses prior experience in commercial consulting, investment banking, private equity diligence, or client-facing analytical roles, demonstrating ability to translate technical findings for business stakeholders
  • Holds a Master's degree in Data Science, Applied Mathematics, Statistics, or related quantitative field with practical application experience
  • Can demonstrate experience working with financial data, alternative datasets, or ESG metrics, showing understanding of investment decision-making processes

📝 Tips for Applying to SESAMm

1

Highlight specific experience with alternative datasets (ESG signals, financial statements, market data) in your resume, not just generic data science projects

2

Quantify your impact in previous roles using metrics relevant to investment decisions (e.g., 'improved model accuracy by X% leading to Y investment recommendation')

3

Tailor your Python examples to financial data analysis, mentioning specific libraries like Pandas for financial time series or NLP tools for sentiment analysis

4

Demonstrate your client-facing experience by describing how you've communicated complex analytical results to non-technical stakeholders in investment contexts

5

Research SESAMm's specific ESG and sentiment analytics products and mention how your skills align with their published case studies or white papers

✉️ What to Emphasize in Your Cover Letter

['Your experience with financial/alternative data analysis and how it relates to private equity investment decisions', 'Specific examples of building and evaluating ML models for business impact, particularly in investment or financial contexts', 'Your ability to bridge technical analysis and business communication, especially with investment professionals', "Why you're specifically interested in SESAMm's focus on ESG and sentiment analytics rather than general data science roles"]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → SESAMm's specific ESG and sentiment analytics products and how they're used by private equity firms
  • → Recent case studies or white papers published by SESAMm on their website to understand their technical approach
  • → The competitive landscape of alternative data providers in finance and how SESAMm differentiates itself
  • → Current trends in ESG investing and how data science is transforming private equity due diligence

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk me through how you would approach feature engineering for ESG signal extraction from unstructured text data
2 Describe a time you had to explain complex statistical model results to investment professionals with limited technical background
3 How would you design a data pipeline for integrating financial statements with alternative datasets like news sentiment?
4 What metrics would you use to evaluate an ML model's performance for private equity investment recommendations?
5 How do you stay current with developments in ESG analytics and alternative data applications in finance?
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⚠️ Common Mistakes to Avoid

  • Presenting only generic data science projects without financial/alternative data context
  • Failing to demonstrate understanding of how data science applies to investment decision-making processes
  • Not having specific examples of client-facing or stakeholder communication in analytical roles
  • Overemphasizing academic theory without showing practical application to business problems

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to SESAMm!

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