Data Science Engineering

Data Science Engineering

Posted 1 day ago by 1761387261

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Negotiable
Outside
Remote
USA
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Airflow Apache Airflow Apache Kafka Artificial Intelligence Automation Azure Databricks Cloud Computing Cloud Technology Computer Science Continuous Integration Continuous Integration and Continuous Delivery Data Architecture Databricks Data Governance Data Science Extract Transform Load (ETL) Generic Programming Google Cloud Google Cloud Platform (GCP) Machine Learning Market Trend Microsoft Azure Python (Programming Language) PyTorch (Machine Learning Library) Scikit-learn (Machine Learning Library) Software Development SQL (Programming Language) Stakeholder Management Workflows

Summary: The Data Science Engineering role involves leading the design, development, and deployment of scalable data science and machine learning solutions. The position requires collaboration with cross-functional teams to implement data-driven strategies and ensure the reliability and performance of data systems. Candidates should possess extensive experience in data science and machine learning, along with strong technical skills in relevant programming languages and cloud platforms. The role also includes mentoring junior team members and contributing to the organization's long-term data and AI strategy.

Key Responsibilities:

  • Lead the end-to-end design, development, and deployment of scalable data science and machine learning solutions.
  • Architect and optimize robust data pipelines, ETL workflows, and feature stores for model training and inference.
  • Collaborate with cross-functional teams (data engineers, analysts, software developers, and business stakeholders) to define and implement data-driven strategies.
  • Develop, train, and validate advanced statistical and machine learning models for predictive and prescriptive analytics.
  • Ensure the reliability, security, and performance of data systems through best practices in data governance and engineering.
  • Drive automation, model monitoring, and continuous integration of ML models into production environments (MLOps).
  • Evaluate emerging technologies and frameworks to improve system efficiency and analytical capabilities.
  • Mentor and guide junior data scientists and engineers in best practices for modeling, data architecture, and experimentation.
  • Communicate complex analytical insights clearly to executive and non-technical stakeholders.
  • Contribute to long-term data and AI strategy, aligning with organizational goals and industry trends.

Key Skills:

  • Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field (Ph.D. preferred).
  • 10+ years of hands-on experience in data science, machine learning, and large-scale data systems.
  • Expertise in Python, SQL, and one or more ML frameworks (TensorFlow, PyTorch, Scikit-learn).
  • Strong knowledge of cloud platforms (AWS, Azure, or Google Cloud Platform) and data tools (Spark, Databricks, Airflow, Kafka).
  • Proven ability to lead complex data initiatives and deliver measurable business impact.

Salary (Rate): undetermined

City: undetermined

Country: USA

Working Arrangements: remote

IR35 Status: outside IR35

Seniority Level: undetermined

Industry: IT

Detailed Description From Employer:

Key Responsibilities

  • Lead the end-to-end design, development, and deployment of scalable data science and machine learning solutions.

  • Architect and optimize robust data pipelines, ETL workflows, and feature stores for model training and inference.

  • Collaborate with cross-functional teams (data engineers, analysts, software developers, and business stakeholders) to define and implement data-driven strategies.

  • Develop, train, and validate advanced statistical and machine learning models for predictive and prescriptive analytics.

  • Ensure the reliability, security, and performance of data systems through best practices in data governance and engineering.

  • Drive automation, model monitoring, and continuous integration of ML models into production environments (MLOps).

  • Evaluate emerging technologies and frameworks to improve system efficiency and analytical capabilities.

  • Mentor and guide junior data scientists and engineers in best practices for modeling, data architecture, and experimentation.

  • Communicate complex analytical insights clearly to executive and non-technical stakeholders.

  • Contribute to long-term data and AI strategy, aligning with organizational goals and industry trends.

Qualifications

  • Bachelor s or Master s degree in Computer Science, Data Science, Engineering, or related field (Ph.D. preferred).

  • 10+ years of hands-on experience in data science, machine learning, and large-scale data systems.

  • Expertise in Python, SQL, and one or more ML frameworks (TensorFlow, PyTorch, Scikit-learn).

  • Strong knowledge of cloud platforms (AWS, Azure, or Google Cloud Platform) and data tools (Spark, Databricks, Airflow, Kafka).

  • Proven ability to lead complex data initiatives and deliver measurable business impact.

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