Mid Level Scientist Jobs
Mid level scientist jobs go to professionals ready to own experiments and projects end to end, interpret results with minimal oversight, and guide junior team members through complex research challenges. Roles are spread across on-site, hybrid, and remote settings in Technology & Software, Consulting & Professional Services, and Retail, with employers like Amazon, Thermo Fisher Scientific, and Alvarez & Marsal hiring at this level now.
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About ABC Legal Services
ABC Legal Services is the nation's premier process serving and court filing company, operating across all 50 states. We are a technology-forward legal services company on a mission to make legal processes faster, smarter, and more reliable. Our data and engineering teams are central to that mission — building models and systems that power operations at scale. We're now looking for a Data Scientist with strong ML engineering and MLOps experience to help us take our machine learning capabilities to the next level.
Role Overview
We're seeking a Data Scientist with hands-on experience in machine learning engineering and MLOps. In this role, you'll own the full model lifecycle — from research and experimentation through deployment, monitoring, and iteration. You'll work within our AWS SageMaker Studio environment and collaborate closely with engineering, operations, and product teams to deliver models that drive measurable business outcomes.
Key Responsibilities
- Develop, train, and evaluate machine learning models to solve business problems across operations, legal services, and marketing
- Own the full ML lifecycle: data preparation, feature engineering, model training, validation, deployment, and monitoring
- Build and maintain MLOps pipelines using AWS SageMaker Studio, including experiment tracking, model registry, and automated retraining workflows
- Partner with product and operations teams to translate business requirements into data science solutions
- Monitor deployed models in production, identify performance degradation, and drive continuous improvement
- Document methodologies, model performance benchmarks, and technical decisions for internal knowledge sharing
- Stay current with advances in ML and data science tooling, and advocate for best practices across the team
Requirements
Required
- 3+ years of experience in data science or a closely related role, with demonstrated ML engineering and MLOps responsibilities
- Strong proficiency in Python for data science and ML development (pandas, scikit-learn, PyTorch or TensorFlow)
- Hands-on experience with AWS SageMaker Studio for model development, training, and deployment
- Solid understanding of MLOps principles: model versioning, pipeline automation, drift detection, and production monitoring
- Experience with SQL and working with structured data in cloud data warehouses or relational databases
- Proven ability to translate complex data science findings into clear, actionable insights for non-technical stakeholders
- Strong self-direction and communication skills suited for a remote work environment
Nice to Have
- Experience in the legal, collections, or financial services industry
- Background in targeted mail marketing, direct mail modeling, or customer segmentation
- Familiarity with AI coding agents and agentic development workflows (e.g., Claude, Copilot, Cursor, or similar tools)
- Experience with propensity modeling, uplift modeling, or response prediction
- Exposure to LLM-based workflows or applied NLP in a production setting
- Data engineering experience with modern tooling such as Dagster, Airbyte, and dbt
- Familiarity with AWS data services including Glue, Lambda, Redshift, and Step Functions
Compensation & Benefits
- Fully remote position with flexible working hours
- Comprehensive health, dental, and vision insurance
- 401(k) with company match
- Paid time off and company holidays
- Opportunity to shape data science strategy at a growing, industry-leading company
Salary Range: $110,000-$130,000 depending on experience
About ABC Legal Services
ABC Legal Services is the nation's premier process serving and court filing company, operating across all 50 states. We are a technology-forward legal services company on a mission to make legal processes faster, smarter, and more reliable. Our data and engineering teams are central to that mission — building models and systems that power operations at scale. We're now looking for a Data Scientist with strong ML engineering and MLOps experience to help us take our machine learning capabilities to the next level.
Role Overview
We're seeking a Data Scientist with hands-on experience in machine learning engineering and MLOps. In this role, you'll own the full model lifecycle — from research and experimentation through deployment, monitoring, and iteration. You'll work within our AWS SageMaker Studio environment and collaborate closely with engineering, operations, and product teams to deliver models that drive measurable business outcomes.
Key Responsibilities
- Develop, train, and evaluate machine learning models to solve business problems across operations, legal services, and marketing
- Own the full ML lifecycle: data preparation, feature engineering, model training, validation, deployment, and monitoring
- Build and maintain MLOps pipelines using AWS SageMaker Studio, including experiment tracking, model registry, and automated retraining workflows
- Partner with product and operations teams to translate business requirements into data science solutions
- Monitor deployed models in production, identify performance degradation, and drive continuous improvement
- Document methodologies, model performance benchmarks, and technical decisions for internal knowledge sharing
- Stay current with advances in ML and data science tooling, and advocate for best practices across the team
Requirements
Required
- 3+ years of experience in data science or a closely related role, with demonstrated ML engineering and MLOps responsibilities
- Strong proficiency in Python for data science and ML development (pandas, scikit-learn, PyTorch or TensorFlow)
- Hands-on experience with AWS SageMaker Studio for model development, training, and deployment
- Solid understanding of MLOps principles: model versioning, pipeline automation, drift detection, and production monitoring
- Experience with SQL and working with structured data in cloud data warehouses or relational databases
- Proven ability to translate complex data science findings into clear, actionable insights for non-technical stakeholders
- Strong self-direction and communication skills suited for a remote work environment
Nice to Have
- Experience in the legal, collections, or financial services industry
- Background in targeted mail marketing, direct mail modeling, or customer segmentation
- Familiarity with AI coding agents and agentic development workflows (e.g., Claude, Copilot, Cursor, or similar tools)
- Experience with propensity modeling, uplift modeling, or response prediction
- Exposure to LLM-based workflows or applied NLP in a production setting
- Data engineering experience with modern tooling such as Dagster, Airbyte, and dbt
- Familiarity with AWS data services including Glue, Lambda, Redshift, and Step Functions
Compensation & Benefits
- Fully remote position with flexible working hours
- Comprehensive health, dental, and vision insurance
- 401(k) with company match
- Paid time off and company holidays
- Opportunity to shape data science strategy at a growing, industry-leading company
Salary Range: $110,000-$130,000 depending on experience
See All 2,021+ Mid Level Scientist Jobs
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Find Mid Level Scientist JobsMid Level Scientist Job Market
Who's Hiring
- Amazon192
- Thermo Fisher Scientific55
- Alvarez & Marsal38
- Apple36
- Terracon Consultants30
Top Industries Hiring
- Technology & Software538
- Consulting & Professional Services265
- Retail261
- E-Commerce & Online Marketplaces222
- Biotechnology & Pharmaceuticals210
Mid Level Scientist Jobs: Frequently Asked Questions
How do I get a mid level scientist job?
Position your existing experience around ownership and independent judgment, not just task completion. Highlight experiments or projects you led, decisions you made without close supervision, and any cross-functional collaboration. Tailor your resume to show the depth of your technical skills and the scope of your contributions. Recruiters at this level want evidence you can drive work forward, not just execute someone else's plan.
Which companies hire mid level scientists?
Companies hiring mid level scientists right now include Amazon, Thermo Fisher Scientific, and Alvarez & Marsal, based on current listings on Migrate Mate as of July 2026. Hiring at this level comes from a wide range of employers, including pharmaceutical companies, biotech startups, industrial manufacturers, government contractors, and research institutions, all of which need scientists who can operate with real autonomy.
Are there remote mid level scientist jobs?
Yes, though the share varies by specialty. About 26% of mid level scientist openings are remote or hybrid as of July 2026, with remote roles most common in data-heavy or computational disciplines. Lab-based and field roles typically require on-site presence, so the remote mix depends heavily on whether the work involves physical materials, equipment, or regulated environments.
How do I move up to a mid level scientist role?
Progression from entry level into a mid level scientist role typically comes from building deeper technical expertise in a defined area, taking on increasing ownership of projects over time, and demonstrating measurable impact through your work. Volunteering to lead smaller studies, mentor newer colleagues, and present findings to broader audiences accelerates the transition and signals readiness for the independence mid level roles require.
Which industries hire the most mid level scientists?
Mid Level scientist roles concentrate in Technology & Software, Consulting & Professional Services, and Retail, based on current listings on Migrate Mate as of July 2026. These sectors drive hiring at this level because they rely on scientists who can manage ongoing research programs, adapt methods to new problems, and contribute to product development or regulatory work without needing step-by-step direction.