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Data Scientist (Data Engineer)

Connection
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What We Do:

CNXN Helix Center at Connection Inc., is at the forefront of AI innovation, offering cutting-edge solutions that redefine the boundaries of artificial intelligence and data management. We are dedicated to helping our clients navigate the complexities of AI integration, ensuring they stay ahead in a rapidly evolving technological landscape. Our commitment to excellence, innovation, and strategic growth makes us an industry leader. Visit us at www.connection.com/helix

Who We Are:

Our team is made stronger by a multitude of backgrounds, experiences, and perspectives. It’s what makes Connection unique—what drives us to innovate and create technology solutions that stand apart from the crowd. We’d love for you to be a part of that fabric, to share your ideas and experiences with a team that thrives on fresh thinking, creativity, and helping others.

Why You Should Join Us:

You’ll find supportive teammates and a rewarding career at Connection—plus great benefits. We take pride in supporting employees with a total rewards package that provides financial, emotional, and physical resources for you and your family. Our compensation, 401k plans, medical insurance, and other benefits are progressive and competitive. We value the importance of our employees’ emotional well-being. To support employees, we provide free therapy visits, mental health coaching and tools, and meditation resources. You’ll also enjoy a generous paid time off package that includes not only vacation and sick time, but also Wellness and Volunteer Time Off days.

This is a remote or hybrid opportunity preferably based near our corporate offices in Merrimack, NH, or Boston or New York metropolitan areas

Job Summary:

Reporting to the VP of Core Engineering and relying on experience and judgement to plan and accomplish goals, the Data Scientist (Data Engineer) is a pivotal role in shaping our data architecture and strategies. The Data Scientist is responsible for designing, implementing, and maintaining robust data pipelines and architectures that support advanced analytics and AI initiatives. The Data Scientist is responsible for the extraction of valuable insights from structured and unstructured data and driving innovation and efficiency across projects.

Responsibilities

  • Develops Data Pipeline. Builds and optimizes data pipelines for efficient data processing and integration.
  • Designs Data Architecture. Designs scalable data architectures that support machine learning and AI applications.
  • Ensures Data Quality. Implements data validation and cleansing procedures to ensure data integrity.
  • Collaborates closely with data scientists, Machine Learning engineers, and software developers to meet project objectives.
  • Optimizes Performance. Monitors system performance and makes recommendations for improvements.
  • Maintains clear documentation of data architectures and processes.
  • Innovates, stays updated on emerging technologies and methodologies to enhance data engineering practices.


 

Requirements

  • Master's Degree in Data Science, Computer Science, Engineering, or related field or the equivalent combination of education and work experience.
  • Minimum 5 years' related work experience.
  • Real world Generative and Predictive foundational experience preferred.
  • Knowledge in identity, security, access management, workflow and/or developer-facing products preferred.
  • Direct experience working with cross-organizational teams.
  • Experience in AI technology policy, compliance, trust and safety policy a plus.
  • Experience in public sector solution delivery a plus.

 

Technical Skills:

  • Programming: Proficient in Python, PyTorch, TensorFlow SQL, and experience with big data tools like DataBricks, SnowFlake.
  • Data Warehousing: Experience with data warehousing solutions like AWS Redshift, Azure Data Fabric, or GCP BigQuery.
  • ETL Tools: Proficiency in ETL tools and data pipeline frameworks.
  • Cloud Platforms: Familiarity with AWS, Azure, or GCP data services.
  • Machine Learning: Basic understanding of ML concepts and how data engineering supports ML workflows.
  • Tools: Experience with version control systems like Git and project management tools like JIRA.

 

Soft Skills:

  • Analytical Mindset: Strong problem-solving skills and attention to detail.
  • Communication: Ability to explain complex data concepts to non-technical stakeholders.
  • Team Collaboration: Proven ability to work effectively in a team environment.
  • Adaptability: Willingness to learn and adapt to new technologies and methodologies.