Senior Data Scientist

inDrive is looking for a Senior Data Scientist to join our team!

Responsibilities

• Design and development of end to end ML systems, covering data collection, annotation, training, validation, deployment, and production monitoring.
• Data analysis and processing, including exploration, cleaning, feature engineering, synthetic data generation, and augmentation to improve model quality.
• Model architecture development and optimization, selecting algorithms, tuning hyperparameters, and leveraging modern approaches in deep learning, reinforcement learning, graph neural networks, or other ML fields.
• Automation of ML pipelines, including data processing, model training, testing, and updating workflows.
• Integration of ML solutions into the company infrastructure, working with backend systems, APIs, and streaming or asynchronous data processing services.
• Monitoring and maintenance of ML models in production, including A/B testing, prediction quality control, debugging, drift detection, and retraining when necessary.
• Research and implementation of new technologies, evaluating cutting edge ML and AI methods, experimenting with novel architectures, and adapting them for business needs.

Qualifications

• Bachelors degree or higher in statistics, mathematics, computer science, or machine learning.
• 5 plus years of experience as an ML engineer, data scientist, or in a similar role.
• Expertise in one or more ML domains, such as computer vision, natural language processing, time series analysis, graph neural networks, or reinforcement learning.
• Experience with modern deep learning techniques, including transformers, diffusion models, or self supervised learning.
• Strong background in production ML engineering, including building and maintaining ML models in production, MLOps, CI/CD, monitoring, and A/B testing.
• Proficiency in Python and experience with tools for streaming, batch, and asynchronous data processing such as pandas, NumPy, Dask, Spark, Kafka, or Ray.
• Experience in developing and optimizing ML pipelines using frameworks like Airflow, Kubeflow, or MLflow.
• Familiarity with production deployment technologies for ML models, such as FastAPI, gRPC, TensorFlow Serving, Triton Inference Server, or TorchServe.
• Understanding of backend system principles, including ML model integration, deployment, and monitoring in production environments.
• Experience with SQL and NoSQL databases, such as PostgreSQL, ClickHouse, MongoDB, Redis, or Elasticsearch.
• Hands-on experience with GPU acceleration for training and inference.

Experience with cloud based ML platforms such as AWS, GCP, or Azure and containerization technologies like Docker or Kubernetes is a plus.

Benefits

• Stable salary, official employment.
• Health insurance.
• Hybrid work mode and flexible schedule.
• Relocation package offered for candidates from other regions.
• Access to professional counseling services including psychological, financial, and legal support.
• Discount club membership.
• Diverse internal training programs.
• Partially or fully paid additional training courses.
• All necessary work equipment.