Job Description

abra R&D is looking for an Algorithms Engineer!

abra R&D is looking for an Algorithms engineer who will take part in building the a next‑generation agentic analytics platform- a real‑time database optimized for AI agents at scale.

The product focuses on real‑time decision making, causal understanding, and time‑series intelligence over large, high‑dimensional data streams.

This role is about building the algorithmic core of the system- the models and logic that power analytics, forecasting, and decision intelligence in production.

What you’ll do:

• Design, develop, and own core algorithms for real‑time analytics
• Build causal inference algorithms to model cause‑effect relationships and support decision systems
• Develop time‑series models for forecasting, anomaly detection, and pattern discovery on real‑time data
• Perform algorithmic research: explore approaches, evaluate tradeoffs, and select optimal solutions
• Develop feature engineering and mathematical logic to improve prediction accuracy and stability
• Work with high‑dimensional, large‑scale, and event‑driven datasets
• Define statistical and mathematical methodologies used across the analytics engine
• Integrate algorithms into production systems, with strong focus on performance and reliability
• Design experiments and evaluation frameworks to validate algorithm quality
• Collaborate closely with engineering teams to ensure models run efficiently in real‑time environments

Requirements

• Strong background in algorithms, mathematics, and statistics
• Hands‑on experience developing production‑grade algorithms
• Excellent Python skills for algorithm development

Proven experience with Causal Inference, such as:

• Propensity Score Matching (PSM)
• Inverse Probability Weighting (IPW)
• Difference‑in‑Differences (DiD)
• Instrumental Variables (IV)
• Regression Discontinuity Design (RDD)
• Double Machine Learning (DML) / Causal Forests

Strong experience with Time‑Series Modeling, including:

• ARIMA / SARIMA
• State Space Models / Kalman Filters
• Time‑series anomaly detection and change‑point detection
• Deep learning approaches for time‑series (LSTM / Transformers – advantage)

Experience taking algorithms from research to production

• Ability to work independently and take full ownership of algorithmic components