Application Guide

How to Apply for Data Scientist

at SESAMm

🏢 About SESAMm

SESAMm is a specialized AI company focused exclusively on extracting ESG, sentiment, and thematic insights from over 20 billion articles, providing unique alternative data for corporate and investment decision-making. Unlike general data analytics firms, SESAMm combines natural language processing with financial expertise to deliver actionable intelligence from unstructured text data at massive scale.

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, with a focus on ESG signals and alternative data. You'll build and maintain data pipelines for diverse sources while collaborating closely with investment teams to translate technical findings into business impact.

💡 A Day in the Life

A typical day might involve developing and refining machine learning models to extract ESG signals from news articles, building Python pipelines to process and integrate new alternative datasets, and collaborating with investment teams to translate analytical findings into actionable insights for private equity decisions. You'd balance hands-on coding with cross-functional meetings to ensure your data science work directly supports business objectives.

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Interview Prep

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🎯 Who SESAMm Is Looking For

  • Has 3-5 years of hands-on experience with feature engineering and statistical modeling specifically applied to financial or alternative datasets (ESG, market data, etc.)
  • Demonstrates strong proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL for building efficient data pipelines that handle large, diverse data sources
  • Possesses experience or strong interest in financial analytics, particularly private equity investment decision support or ESG/sentiment analysis
  • 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 projects where you've worked with financial datasets, ESG metrics, or alternative data sources, quantifying your impact on decision-making processes

2

Demonstrate your Python and SQL expertise with concrete examples of data pipelines you've built for large, diverse datasets (mention specific libraries like Pandas and Scikit-learn)

3

Research SESAMm's specific ESG and thematic analytics products and mention how your skills align with their 20+ billion article processing capability

4

Prepare examples of how you've translated complex analytical findings into actionable insights for non-technical stakeholders, particularly in investment contexts

5

If you have cloud experience (AWS, Azure, Databricks), emphasize how you've used these platforms for data processing at scale, as this is explicitly mentioned as a plus

✉️ What to Emphasize in Your Cover Letter

["Your experience with financial/extra-financial datasets and how you've applied feature engineering and statistical modeling to investment decision support", "Specific examples of building and maintaining data pipelines using Python and SQL for large, diverse data sources similar to SESAMm's 20+ billion articles", 'Your ability to bridge technical analysis and business impact, particularly in communicating complex results to investment teams', "Why you're specifically interested in SESAMm's niche focus on AI-driven ESG and thematic 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 thematic analytics products and how they differentiate from competitors in the alternative data space
  • → The company's client base in private equity and corporate sectors to understand how your work would directly impact their decision-making
  • → Recent news or case studies about SESAMm's AI applications for extracting insights from unstructured text data
  • → The specific types of alternative datasets mentioned (financial statements, market data, ESG signals) and how they might intersect in investment analysis

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk through a specific project where you engineered features and built models for financial or alternative datasets, focusing on your methodology and business impact
2 Technical questions about optimizing Python data pipelines for large-scale text processing (20+ billion articles) and handling diverse data sources
3 How you would approach analyzing ESG signals or sentiment data to support private equity investment decisions
4 Scenario-based questions about communicating complex analytical findings to investment professionals with varying technical backgrounds
5 Your experience with cloud data environments (AWS/Azure/Databricks) and how you've used them for scalable data processing
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⚠️ Common Mistakes to Avoid

  • Presenting generic data science projects without connection to financial analytics, ESG, or alternative data processing
  • Failing to demonstrate specific experience with Python data science stack (Pandas, NumPy, Scikit-learn) and SQL for pipeline development
  • Applying with a generic cover letter that doesn't address SESAMm's specific focus on AI-driven ESG and thematic analytics from text data

📅 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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