Mid Level Clinical Data Specialist Jobs
Mid level clinical data specialist jobs go to professionals ready to own data management workflows end to end, drive study database decisions with limited oversight, and guide junior staff through complex clinical trial processes. Hiring runs across Technology & Software, Consulting & Professional Services, and Retail, with 43% remote or hybrid availability and employers like Amazon, Speechify, and Apple competing for clinical data specialists 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 3,887+ Mid Level Clinical Data Specialist Jobs
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Find JobsMid Level Clinical Data Specialist Job Market
Who's Hiring
- Amazon175
- Speechify89
- Apple86
- Google82
- Booz Allen Hamilton58
Top Industries Hiring
- Technology & Software968
- Consulting & Professional Services459
- Retail284
- Banking & Financial Services241
- E-Commerce & Online Marketplaces210
Mid Level Clinical Data Specialist Jobs: Frequently Asked Questions
How do I get a mid level clinical data specialist job?
Position your existing clinical data experience around ownership and outcomes, not just task completion. Highlight studies you managed end to end, discrepancy resolution you led independently, and any work with CDASH or CDISC standards. Applications that show you can operate with minimal supervision and catch data integrity issues before they escalate consistently outperform those that simply list tools and years.
Which companies hire mid level clinical data specialists?
Companies hiring mid level clinical data specialists right now include Amazon, Speechify, and Apple, based on current listings on Migrate Mate as of July 2026. At this level, hiring comes primarily from contract research organizations, pharmaceutical sponsors, and health technology companies that need specialists who can manage study databases with reduced oversight.
Are there remote mid level clinical data specialist jobs?
Yes, remote and hybrid availability is substantial at the mid level. About 43% of mid level clinical data specialist openings are remote or hybrid as of July 2026, reflecting how clinical data work translates well to distributed teams. Sponsors and CROs running multi-site trials have normalized remote data management for experienced specialists.
How do I move up to a mid level clinical data specialist role?
Growing into a mid level role means progressing from completing assigned tasks to owning study data processes. Build depth in clinical data standards like CDASH and CDISC, take on end-to-end database setup for smaller studies, and demonstrate that you can catch and resolve discrepancies without escalating every decision. Measurable impact, such as clean database lock timelines or reduced query rates, signals readiness for the step up.
Which industries hire the most mid level clinical data specialists?
Mid Level clinical data specialist roles concentrate in Technology & Software, Consulting & Professional Services, and Retail, based on current listings on Migrate Mate as of July 2026. These sectors drive mid level hiring because late-phase clinical trials and regulatory submission timelines require experienced data specialists who can work independently across complex, multi-site study databases.