Senior Data Scientist

Charger Logistics Inc.

. 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.

Posted 9/15/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying production-grade ML models, with a strong focus on data pipelines, anomaly detection, and advanced analytics. Proficient in leveraging cloud services and MLOps workflows to optimize model performance and deliver actionable insights.

Highest-signal resume keywords

Machine Learning Model DevelopmentGoogle Cloud Platform (GCP)Python ProgrammingAdvanced SQL SkillsData Pipeline Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData ScienceAnomaly DetectionTime-Series ForecastingStatistical ModelingFeature EngineeringExploratory Data Analysis (EDA)Data VisualizationCloud ServicesETL/Orchestration Tools

Soft Skills

Excellent CommunicationProblem-SolvingCollaboration

Tools & Technologies

BigQueryVertex AIKafkaRisingWaveSnowflakeCloud Composer (Airflow)MatillionPandasNumPyScikit-learn

Certifications & Qualifications

Google Cloud Professional Data EngineerMachine Learning EngineerSnowPro® Advanced: Data Scientist

Industry Keywords

Fleet OptimizationPredictive MaintenanceDriver Behavior AnalysisRetrieval-Augmented Generation (RAG)Geospatial MLGraph MLComputer VisionGPS Data Analysis

Tech Stack

Tools & technologies

AirflowAWSAzureBigQueryCloudETLGoogle Cloud PlatformKafkaMatillionNumpyPandasPostgresPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

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

What you’ll need

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 (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

Comp & perks

Competitive Salary
Healthcare Benefit Package
Career Growth