Data Science Engineer
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Job Description
Data Science Engineer transforming raw data into actionable intelligence at Mindex. Developing machine learning models and establishing MLOps workflows on cloud infrastructure.
Responsibilities:
- Design and implement scalable data pipelines to ingest, process, and transform large datasets (structured & unstructured).
- Develop, validate, and optimize supervised and unsupervised machine learning models leveraging Python, SQL, and modern libraries.
- Conduct feature engineering, model selection, and statistical modeling to deliver high-impact solutions.
- Build and expose model APIs or containerized workflows for seamless integration and deployment in production environments.
- Apply MLOps best practices to model versioning, testing, monitoring, and deployment.
- Work with Big Data technologies such as Databricks and Snowflake to unlock analytics at scale.
- Orchestrate complex workflows using tools like Airflow or Dagster for automation and reliability.
- Collaborate with AI teams to refine prompt engineering and leverage AI tooling for model fine-tuning and augmentation.
- Maintain familiarity with leading cloud platforms (AWS, Azure, GCP) for model training, deployment, and infrastructure management.
- Partner with product, engineering, and business teams to translate requirements into technical solutions.
Requirements:
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Statistics, or a related field.
- 3+ years of experience in data science engineering or related roles.
- Proficiency in Python and SQL for data extraction, analysis, and modeling.
- Strong background in statistical modeling and machine learning algorithms (supervised and unsupervised).
- Experience with feature engineering and end-to-end model development.
- Hands-on experience with MLOps foundations (CI/CD, model monitoring, automated retraining).
- Familiarity with Big Data tools (Databricks, Snowflake, Spark).
- Experience with workflow orchestration platforms such as Airflow or Dagster.
- Understanding of cloud architecture and deployment (AWS, Azure, GCP).
- Experience deploying models as APIs or containers (Docker, FastAPI, Flask).
- Familiarity with prompt engineering techniques and AI tooling for cutting-edge model development.
- Excellent problem-solving and communication skills.
- Experience with advanced AI tools (e.g., LLMs, vector databases).
- Exposure to data visualization tools and dashboarding.
- Knowledge of security, privacy, and compliance in ML workflows.
Benefits:
- Health insurance
- Paid holidays
- Flexible time off
- 401k retirement savings plan and company match with pre-tax and ROTH options
- Dental insurance
- Vision insurance
- Employer paid disability insurance
- Life insurance and AD&D insurance
- Employee assistance program
- Flexible spending accounts
- Health savings account with employer contributions
- Accident, critical illness, hospital indemnity, and legal assistance
- Adoption assistance
- Domestic partner coverage
- Tickets to local sporting events
- Teambuilding events
- Holiday and celebration parties
- Leadership training
- License to Udemy online training courses
- Growth opportunities
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