Data Scientist

Synergy Interactive; Chicago (IL); On-site; USD 120,000 - 150,000; Full time

Job summary

A data solutions company in Chicago is seeking a Specialist Data Scientist with expertise in computer vision and big data analytics to design and deploy scalable data science solutions. The role involves hands-on machine learning, particularly with YOLO-based models, and requires collaboration with cross-functional teams. Candidates should have 8+ years of experience with strong skills in Python, Docker, Kubernetes, and relevant big data technologies. A Master’s or PhD in a related field is preferred.

Qualifications

• 8+ years of experience in data science, machine learning, or big data engineering.
• Strong experience with YOLO-based computer vision models and modern ML frameworks.
• Experience with big data technologies such as Hadoop, Spark, Hive, and NoSQL databases.

Responsibilities

• Lead the design and implementation of scalable big data solutions and advanced analytics frameworks.
• Develop, fine-tune, and retrain YOLO-based computer vision models for object detection.
• Design and optimize data models, pipelines, and algorithms to solve complex business challenges.

Skills

YOLO-based computer vision models

Machine learning frameworks

Python

Docker

Kubernetes

Google Cloud Platform

Hadoop

Spark

NoSQL databases

Education

Master’s or PhD in Data Science, Computer Science, Engineering, or related field

Tools

FastAPI

Kafka

Flink

Job description

We are seeking a Specialist Data Scientist with strong expertise in computer vision and big data analytics to design, optimize, and deploy scalable, production-grade data science solutions. This role blends hands-on machine learning—particularly YOLO-based object detection—with big data engineering and cloud-native deployment. You will work closely with cross-functional teams to turn complex datasets into actionable, real-time insights that drive measurable business impact.

Key Responsibilities

• Lead the design and implementation of scalable big data solutions and advanced analytics frameworks.
• Develop, fine-tune, and retrain YOLO-based computer vision models for object detection use cases.
• Optimize models for edge and production deployment using techniques such as quantization and distillation.
• Build and deploy machine learning models integrated into production systems for real-time insights.
• Design and optimize data models, pipelines, and algorithms to solve complex business challenges.
• Leverage big data technologies (Hadoop, Spark, NoSQL) to process and analyze large-scale datasets.
• Containerize FastAPI-based services using Docker and publish results to message brokers.
• Operate within Kubernetes-based production environments on Google Cloud Platform.
• Collaborate with stakeholders to translate business requirements into data-driven strategies.
• Continuously improve data science and analytics processes for efficiency, scalability, and impact.

Required Skills & Experience

• 8+ years of experience in data science, machine learning, or big data engineering.
• Strong experience with YOLO-based computer vision models and modern ML frameworks.
• Proficiency in Python; experience with TypeScript is a plus.
• Hands-on experience with Docker for containerization and service deployment.
• Working knowledge of Kubernetes and Google Cloud Platform in production environments.
• Experience with big data technologies such as Hadoop, Spark, Hive, and NoSQL databases (e.g., Cassandra, MongoDB).
• Strong background in statistical analysis, data mining, and predictive modeling.
• Experience building scalable architectures and optimizing data pipelines (ETL).
• Strong problem-solving skills and the ability to make data-driven decisions in complex environments.

Top Skills Needed

• YOLO-based models & machine learning frameworks

Set Yourself Apart With

• Master’s or PhD in Data Science, Computer Science, Engineering, or a related field.
• Experience deploying enterprise-scale big data or ML solutions (finance, healthcare, retail a plus).
• Experience with real-time data and stream processing (Kafka, Flink).
• Familiarity with BI tools and presenting insights to non-technical stakeholders.
• Experience deploying data science solutions using Docker and Kubernetes.