Mid Level Data Scientist Jobs
Mid level data scientist jobs go to analysts ready to own pipelines end to end, translate findings into decisions, and guide junior teammates with limited oversight. Openings are spread across on-site, remote, and hybrid settings in Technology & Software, Consulting & Professional Services, and Retail, with Apple, Alvarez & Marsal, and Whatnot 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
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Find Mid Level Data Scientist JobsMid Level Data Scientist Job Market
Who's Hiring
- Apple26
- Alvarez & Marsal26
- Whatnot22
- Booz Allen Hamilton21
- TikTok19
Top Industries Hiring
- Technology & Software209
- Consulting & Professional Services78
- Retail69
- Banking & Financial Services60
- Investment & Asset Management43
Mid Level Data Scientist Jobs: Frequently Asked Questions
How do I get a mid level data scientist job?
Position yourself around ownership: applications that show you drove a project from data collection through production deployment stand out. Highlight work where your analysis changed a business decision, not just where you ran models. Tailor your resume to the domain the employer works in, and be ready to walk through your methodology and results in detail during interviews.
Which companies hire mid level data scientists?
Companies hiring mid level data scientists right now include Apple, Alvarez & Marsal, and Whatnot, based on current listings on Migrate Mate as of July 2026. Hiring at this level comes from a mix of established technology firms, financial services companies, healthcare organizations, and fast-growing startups that need scientists who can work independently and deliver without heavy supervision.
Are there remote mid level data scientist jobs?
Yes, remote and hybrid options are common at this level. About 43% of mid level data scientist openings are remote or hybrid as of July 2026, reflecting how many data teams have shifted to distributed work. Fully on-site roles still exist, particularly in regulated industries like healthcare and finance where data governance is stricter.
How do I move up to a mid level data scientist role?
Growth from entry level to mid level comes from accumulating ownership, not just time. Focus on leading a project end to end, building something that other teams depend on, and quantifying the impact of your work in business terms. Deepening expertise in one domain or technique, such as NLP, causal inference, or ML deployment, also accelerates the transition and gives hiring managers a clear reason to level you up.
Which industries hire the most mid level data scientists?
Mid Level data scientist roles concentrate in Technology & Software, Consulting & Professional Services, and Retail, based on current listings on Migrate Mate as of July 2026. Those sectors drive hiring at this level because they generate large, complex datasets that require scientists who can work independently, build reliable pipelines, and connect analytical outputs directly to product or operational decisions.