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Data Science Engineer

Data ScientistData ScientistFull TimeRemoteTeam 201-500Since 1998H1B SponsorCompany Site LinkedIn

Location

New York

Posted

84 days ago

Salary

$90K - $120K / year

Bachelor Degree3 yrs expEnglishAirflowAWSAzureCloudDockerFlaskGoogle Cloud PlatformPythonSparkSQL

Job Description

• 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.

Job 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.