We are looking for a Data Science Engineer / Senior Research Engineer to join our applied AI team at Abhibus. This is a hybrid role at the intersection of AI/ML, classical data science, and data engineering.
You’ll be responsible for:
- Designing and building AI-driven features that power search, personalization, pricing, recommendations, and fraud detection.
- Developing robust data pipelines and scalable infrastructure to ensure reliable ML model training and deployment.
- Generating statistical and business insights from large-scale bus travel data to shape product strategy.
- Your work will touch millions of travellers and hundreds of bus operators, bringing data- driven innovation to the mobility ecosystem
Key Responsibilities:
- Design and implement ML models for personalization, demand forecasting, route optimization, pricing, anomaly detection, and recommendations.
- Build, maintain, and optimize ETL/data pipelines to feed ML models and analytics dashboards.
- Work with product managers & engineers to translate business requirements →models → production systems.
- Perform statistical analysis, A/B testing, and causal inference for product & growth experiments.
- Read and adapt academic research papers into practical product applications.
- Ensure model lifecycle management: data collection, feature engineering, training, deployment, monitoring, and retraining.
- Collaborate with data engineering team on data architecture, warehousing, and scaling pipelines.
- Background in Computer Science, Engineering, or Mathematics (top institutes preferred).
- 2–6 years of experience in data science & engineering.
- Strong fundamentals in algorithms, data structures, ML/DL, and statistics.
- Proficiency in Python and libraries like TensorFlow/PyTorch, Scikit-learn, Pandas, NumPy, SciPy.
- Hands-on experience with SQL and data pipelines (Airflow, Spark, Kafka, dbt, or equivalent).
- Ability to translate ambiguous problems into structured models and scalable solutions
Preferred Skills:
- Experience with cloud platforms (AWS, GCP, Azure) and ML deployment tools (SageMaker, MLflow, Kubeflow).
- Knowledge of big data technologies (Spark, Hadoop, Presto, ClickHouse).
- Experience with travel, mobility, or marketplace problems.
- Contributions to open-source ML or published research
Candidates are responsible for safeguarding sensitive company data against unauthorized access, use, or disclosure, and for reporting any suspected security incidents in line with the organization's ISMS (Information Security Management System) policies and procedures.
