We are looking for a
Data Scientist to develop, deploy, and scale machine learning (ML) and AI solutions for fleet analytics, logistics optimization, and operational decision-making. This is a hands-on role focusing on production-grade ML, real-time and streaming analytics, and AI-driven decision systems built on cloud platforms, including Google Cloud, Kafka, and RisingWave.
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, supporting model training, deployment, monitoring, and drift detection.
- 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.
- Perform exploratory data analysis (EDA) to uncover trends, anomalies, and business insights.
- Build dashboards and visualizations for stakeholders.
- Collaborate with cross-functional teams to translate business problems into robust data science solutions.
- Support best practices in model development, experimentation, documentation, and data governance.
Requirements- Bachelor's degree or equivalent in Data Analytics, Statistics, Mathematics, or Computer Science.
- 4+ 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 (training, pipelines, deployment, feature store) and BigQuery (data modeling, performance tuning).
- 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, including testing, 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 (Retrieval-Augmented Generation), with familiarity of emerging approaches like KAG (Knowledge-Augmented Generation) and CAG (Cache-Augmented Generation).
- Experience with Azure, AWS, GCP, Databricks, or multi-cloud deployments is a plus.
- Excellent communication and problem-solving skills, with the ability to thrive in fast-paced environments.
- Certifications: Google Cloud Professional Data Engineer or Machine Learning Engineer is an asset; SnowPro® Advanced: Data Scientist certification preferred.
Benefits- Competitive Salary
- Healthcare Benefit Package
- Career Growth
