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Aspire

Data Scientist - Machine Learning

Aspire

Staffing and Recruiting · 11-50 employees

Jul 22
Remote machine-learning Mid (2-5 yrs) Full-time
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About the role

You will be responsible for developing, deploying, and maintaining production-grade machine learning models within a cloud environment. The role involves collaborating with cross-functional teams to build scalable data pipelines and enhance product offerings using AI and LLM capabilities.

What they look for

Python Machine learning AWS SageMaker SQL Git MLOps MLflow Airflow Dbt Snowflake Causal inference LLMs AI agents Agile Feature engineering Data pipelines

Requirements

Candidates must have at least 2 years of experience in production machine learning and strong proficiency in Python and SQL. Experience with enterprise ML platforms like AWS SageMaker and MLOps tools is required to support independent work in production environments.

Benefits

Competitive total compensation package Performance-based bonus Remote-first environment Technical and non-technical training programs Global exposure International technology conferences

Full description

This is a remote position.

About the Role

As a Data Scientist at Aspire, you will be responsible for building and maintaining production machine learning solutions in a cloud environment. This role focuses on developing scalable ML models, collaborating with cross-functional teams, and supporting data-driven decision making across the organization. You will work within US time zones (PST to EST) and operate in a remote-first, distributed team environment.

What You'll Do

  • Develop, deploy, and maintain production machine learning models.
  • Perform exploratory data analysis and feature engineering to extract actionable insights.
  • Build and improve data pipelines that support ML workflows and automation.
  • Collaborate with engineering and business stakeholders to deliver scalable ML solutions.
  • Contribute to AI/LLM-based capabilities where applicable to enhance product offerings.
  • Monitor and optimize model performance in production environments (not only notebook-based development).
  • Document all procedures, configurations, and changes in a clear, auditable manner.

What You'll Need

  • 2+ years of experience building and maintaining production machine learning models.
  • Strong Python programming skills.
  • Experience with AWS SageMaker or another enterprise ML platform (e.g., Vertex AI or Azure ML) supporting production ML pipelines.
  • Experience deploying and monitoring ML models in production (not only notebook-based development).
  • Advanced SQL.
  • Git/version control.
  • Ability to work independently in production environments.
  • Experience with MLOps tools (MLflow, Airflow, dbt, or similar).
  • Snowflake.
  • Marketing, growth, experimentation, or causal inference experience.
  • Experience with LLMs or AI agents.
  • Agile development practices.
  • Experience building scalable ML solutions in enterprise environments.
  • Familiarity with cloud-based ML platforms and production deployment best practices.
  • Strong communication skills and ability to work with cross-functional teams.
  • Familiarity with US time zones (PST to EST) and remote collaboration workflows.

Why Aspire

​

In addition to a competitive long-term total compensation package with salary and performance-based bonus, we have a reward philosophy that goes beyond compensation.

  • Be part of a remote-first organization where flexibility is embraced.
  • Work and learn alongside talented engineers and technology leaders.
  • Explore opportunities to learn and grow through technical and non-technical training programs.
  • Gain global exposure by working on products with international teams and clients.
  • Attend virtual and in-person international technology conferences to expand your knowledge and network.

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