Job Description

abra R&D is looking for a Data Scientist!

abra R&D is looking for a Data Scientist who will take part in building the next‑generation Database built from scratch for AI Agents

agentic analytics platform that enables data‑driven decision making on large‑scale, real‑time data.

The product focuses on extracting insights, understanding patterns, and supporting decisions through advanced analytics, experimentation, and statistical modeling.

The Data Scientist role focuses on analysis, modeling, experimentation, and insights, rather than low‑level algorithm or infrastructure development.

What you’ll do:

• Analyze large‑scale structured and unstructured datasets to extract actionable insights
• Build and evaluate statistical and machine learning models to support analytics and decision systems
• Apply causal inference methodologies to understand relationships in data and measure impact
• Develop time‑series models for forecasting, trend analysis, and anomaly detection
• Define statistical methodologies used across analytics and decision‑making processes
• Design and analyze experiments (A/B tests, uplift modeling, evaluation frameworks)
• Create data‑driven feedback loops to improve product decisions
• Collaborate with AI and engineering teams to translate analytical insights into product features
• Communicate findings clearly to technical and non‑technical stakeholders

Requirements

• 7+ years of experience in Data Science, Applied Machine Learning, or Quantitative Analysis
• M.Sc or PhD in Statistics, Mathematics, Computer Science, or a related field
• Strong experience with Python for data analysis and modeling
• Strong experience with Time‑Series Analysis, including forecasting and anomaly detection
• Hands‑on experience with machine learning libraries such as scikit‑learn, XGBoost, LightGBM
• Strong data manipulation skills using Pandas, NumPy, and large‑scale data tools (e.g., Spark)
• Ability to translate data analysis into business and product insights

Proven experience with Causal Inference, including methods such as:

• Propensity Score Matching (PSM)
• Inverse Probability Weighting (IPW)
• Difference‑in‑Differences (DiD)
• Instrumental Variables (IV)
• Regression Discontinuity Design (RDD)

Strong Plus

• Experience applying causal inference in real product environments
• Background in analytics platforms or data‑driven products
• Experience working with event‑based or real‑time data
• Experience designing and analyzing experimentation platforms
• Familiarity with LLM‑based or agentic analytics systems