Data Scientist

• Conduct in-depth data analysis to support private equity investment decisions, developing and implementing analytical models including feature engineering, statistical modeling, and machine learning to extract insights from complex financial and extra-financial datasets.
• Design and maintain efficient data pipelines for collecting, cleaning, and integrating large, diverse data sources (financial statements, market data, ESG signals, alternative datasets, etc.) using Python and SQL.
• Work closely with investment, operations, and data science teams to identify high-impact analytical use cases and communicate complex results clearly to both technical and non-technical stakeholders.

This role contributes to a groundbreaking AI platform that transforms investment analysis in private equity, helping clients make better data-driven decisions and manage risk through advanced AI and NLP technologies applied to ESG factors and other critical business intelligence.

• Master’s degree in Data Science, Applied Mathematics, Statistics, or a related quantitative field.
• 3–5 years of hands-on experience in data science or analytics, including internships, with proven experience in feature engineering, statistical modeling, and building/evaluating ML models.
• Strong proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL, with familiarity with cloud data environments (AWS, Azure, or Databricks) as a plus.
• Prior experience in commercial consulting, investment banking, private equity diligence, or a client-oriented analytical role is required, with strong communication skills and fluent English.

• Flexibility: Team members can work remotely and collaborate with colleagues around the world.
• Career Development: Rapid company growth creates ongoing opportunities for personal and professional growth, with structured training, mentorship, and access to educational platforms.
• Global Perspective: Collaborate with teams across Europe and the United States, gaining international experience in a fast-paced environment.
• Transparency: Monthly "Ask Me Anything" meetings, frequent business updates, and strategy-sharing discussions keep employees informed.
• Well-being: Focus on work/life balance, teamwork, and a supportive, learning-oriented culture.

SESAMm extracts behavioral insights from millions of unstructured text data sources across the public internet in over 100 languages, helping clients distill valuable intelligence on businesses and their customers to make investment decisions and manage risk, with a focus on applying Natural Language Processing and AI algorithms to track e-reputation, competitive positioning, sentiment analysis, risk, and ESG factors.