Sr. Data Scientist
New
C
Charger Logistics Inc.Logistics, Transportation
United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 6+ years
- Required Skills
- PythonSQLGCPKafkaMachine LearningSnowflakeBigQueryMLOps
Requirements
- Bachelor’s degree or equivalent in Data Analytics, Statistics, Mathematics, or Computer Science.
- 6+ years of hands-on experience in data science and machine learning/AI, delivering production-grade ML solutions.
- Strong experience in Python, including libraries such as Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, XGBoost, and LightGBM.
- Advanced SQL skills, including CTEs, window functions, and query optimization.
- Hands-on experience with Google Cloud, including Vertex AI and BigQuery.
- Experience with streaming platforms (Kafka, RisingWave) and Snowflake.
- Knowledge of anomaly detection, time-series forecasting, optimization, and applied statistical modeling.
- Experience deploying and monitoring ML models in production and working with ETL/orchestration tools like Matillion, Airflow, and Cloud Composer.
- Familiarity with advanced ML and AI techniques, including LLMs, geospatial or graph ML, computer vision, and GPS data analysis.
- Solid understanding of knowledge retrieval patterns including RAG, KAG, and CAG.
Responsibilities
- Design, develop, and deploy production-grade ML models for fleet optimization, including route optimization, ETA prediction, fuel efficiency, capacity planning, predictive maintenance, and driver behavior analysis.
- Build anomaly detection, forecasting, and time-series models to monitor vehicle health, trip deviations, fuel theft, and demand fluctuations.
- Develop batch and real-time ML pipelines with low-latency inference using Kafka, RisingWave, and cloud services.
- Integrate large language models (OpenAI, Google MCP, Ollama, Hugging Face) for conversational analytics, automated insights, and retrieval-augmented generation (RAG) systems.
- Operate MLOps workflows on Google Cloud using Vertex AI Pipelines, Feature Store, and Model Registry.
- Build and optimize end-to-end data pipelines for analytics and ML using BigQuery, Dataflow, Dataproc, Vertex AI, Cloud Functions, Pub/Sub, and Cloud Composer (Airflow).
- Design scalable analytical data models in BigQuery, AlloyDB PostgreSQL, and Snowflake; optimize SQL-based feature engineering, data partitioning, and clustering.
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