Data Scientist - LLM Models

Description :

Responsibilities :

- Design predictive models for trajectory forecasting, traffic participants behavior, and crossing probabilities.

- Develop risk scoring mechanisms using time-shifted risk prediction and sliding time windows.

- Implement multi-agent reinforcement learning (MARL) frameworks to simulate and train cooperative behaviors.

- Work with simulation teams to integrate ground truth scenarios and replayable datasets.

- Build scoring algorithms for different data dimensions based on the severity and impact.

- Evaluate model performance using precision, recall, and event-level accuracy.

- Collaborate with data engineers to define feature pipelines and streaming inputs.

Requirements :

- 3+ years of experience in applied data science, preferably in real-time or simulationbased environments.

- Strong proficiency in Python, NumPy, Pandas, and deep learning frameworks like PyTorch or TensorFlow.

- Experience with time-series analysis, Bayesian models, or probabilistic forecasting.

- Understanding of reinforcement learning, especially multi-agent settings.

- Knowledge of vehicle kinematics, trajectory forecasting, or intelligent transportation systems.

Nice To Have :

- Experience with simulation environments like CARLA, SUMO or VISSIM simulation data.

- Prior work on ADAS, or smart city risk management.

- Familiarity with CEP engines or event stream analytics tools.

- Understanding of data fusion from camera, LiDAR and other infrastructure inputs.

Must Have :

- Python- high level coding exp is a must.

- Traditional ML

- AWS, Databricks, airflow, Gitlab

- LLM, Gen