Application Guide

How to Apply for Data Scientist

at SESAMm

🏢 About SESAMm

SESAMm is a specialized AI company that analyzes over 20 billion articles to generate ESG, sentiment, and thematic analytics for corporate insights. What makes SESAMm unique is its focus on alternative data and AI-driven insights specifically for investment decisions, bridging the gap between traditional financial analysis and modern data science. Someone might want to work there to apply data science to real-world financial impact while working with massive, diverse datasets.

About This Role

This Data Scientist role at SESAMm involves developing analytical models to support private equity investment decisions by extracting insights from complex financial and extra-financial datasets. The role is impactful because it directly influences investment strategies through data-driven insights, requiring both technical model-building skills and the ability to communicate findings to investment teams.

💡 A Day in the Life

A typical day might involve developing and refining ML models to extract insights from ESG and sentiment data, then cleaning and integrating new financial datasets into existing pipelines. You'd likely collaborate with investment teams to understand their analytical needs and present your findings, while also working with data science colleagues to improve model performance and data infrastructure.

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🎯 Who SESAMm Is Looking For

  • Has a Master's in a quantitative field plus 3-5 years of hands-on experience with feature engineering, statistical modeling, and ML model evaluation in a professional setting
  • Demonstrates strong proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL, with practical experience building data pipelines for diverse datasets
  • Has experience or strong interest in financial/ESG data analysis, with the ability to work with both structured financial data and unstructured alternative data sources
  • Can effectively communicate complex analytical results to both technical data science teams and non-technical investment stakeholders

📝 Tips for Applying to SESAMm

1

Highlight specific experience with ESG data, sentiment analysis, or thematic analytics in your resume, as this is core to SESAMm's business

2

Quantify your impact with metrics from previous data science projects, especially those involving financial data or investment decisions

3

Showcase projects where you've worked with diverse data sources (financial statements, market data, alternative datasets) in your portfolio

4

Mention any experience with cloud data environments (AWS, Azure, Databricks) even if it's limited, as this is listed as a plus

5

Tailor your application to show how your skills align with private equity or investment decision support, not just general data science

✉️ What to Emphasize in Your Cover Letter

['Explain your experience with financial/ESG data analysis and how it relates to investment decision-making', "Describe specific projects where you've built data pipelines for diverse datasets using Python and SQL", 'Highlight your ability to communicate complex results to both technical and non-technical stakeholders', "Connect your background to SESAMm's specific focus on AI-driven analytics from massive article datasets"]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Study SESAMm's specific products and services in ESG, sentiment, and thematic analytics to understand their business model
  • → Research how alternative data and AI analytics are used in private equity investment decisions
  • → Look into recent news or case studies about SESAMm's work with financial institutions
  • → Understand the competitive landscape of AI-driven financial analytics companies

💬 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 sentiment analysis from news articles
2 Describe a time you built a data pipeline for integrating financial statements with alternative datasets
3 How would you explain a complex ML model's results to a private equity investment team with limited technical background?
4 What challenges have you faced working with large, diverse datasets and how did you overcome them?
5 How would you validate that your analytical models are actually improving investment decisions?
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⚠️ Common Mistakes to Avoid

  • Applying with only generic data science experience without showing relevance to financial/ESG data analysis
  • Failing to demonstrate experience with the complete data pipeline (collection, cleaning, integration, modeling)
  • Overemphasizing academic ML theory without showing practical application to business problems, especially in finance

📅 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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