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Data Scientist / ML Engineer
INNOCV Solutions. Design, develop, and maintain scalable data pipelines using PySpark to process large volumes of data .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and maintaining scalable data pipelines using PySpark and optimizing Machine Learning models with AWS SageMaker. Proficient in advanced data analysis with Python and SQL, while applying MLOps best practices for production environments.
Highest-signal resume keywords
PySpark Data ProcessingMachine Learning Model DevelopmentAWS SageMakerSQL Data AnalysisMLOps Practices
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonPandasNumPyScikit-learnSQLMachine Learning AlgorithmsData EngineeringData PipelinesPredictive AnalyticsHyperparameter Optimization
Soft Skills
Analytical ThinkingProblem-SolvingCuriosityAttention to DetailCollaboration
Tools & Technologies
AWS AthenaGitGitHubAgile Development
Industry Keywords
Data EngineeringMachine LearningMLOpsData AnalysisProduction Environments
Tech Stack
Tools & technologiesAWSNumpyPandasPySparkPythonScikit-LearnSQL
About the role
Key responsibilities & impact- Design, develop, and maintain scalable data pipelines using PySpark to process large volumes of data
- Develop, test, and optimize Machine Learning models for predictive analytics
- Manage and deploy Machine Learning environments and workflows using AWS SageMaker and Athena
- Integrate Machine Learning models into production pipelines
- Monitor, maintain, and continuously improve production ML models through MLOps best practices
- Collaborate with cross-functional and multidisciplinary teams to understand business challenges and develop data-driven solutions
- Conduct advanced data analysis using Python and SQL to deliver actionable insights
- Work collaboratively using Git/GitHub and agile development methodologies
Requirements
What you’ll need- 2–3 years of experience in Data Engineering, Machine Learning, or similar roles
- Advanced proficiency in Python, including Pandas, NumPy, and Scikit-learn
- Strong SQL skills for data querying, transformation, and analysis
- Hands-on experience with PySpark for large-scale data processing
- Solid understanding of Machine Learning algorithms, including regression, classification, clustering, and hyperparameter optimization techniques
- Experience with AWS services, especially SageMaker and Athena
- Knowledge of MLOps practices and experience integrating and monitoring ML models in production environments
- Familiarity with Git and collaborative development workflows using GitHub
- Residence in Madrid
- Strong analytical thinking and problem-solving skills
- Curiosity, autonomy, attention to detail, and a collaborative mindset
Benefits
Comp & perks- Professional development through training, tech breakfasts, and attendance at forums
- Personal budget for staying up to date with market trends
- Flexible hours
- Special timetable: Fridays and summer 7h
- Every three years, 6 extra days off
- Day off on your birthday
- Referral benefit for bringing a friend
- Flexible compensation
- Wellness - Gympass
- Volunteering opportunities
- Company events and team buildings
- Work-life balance and flexibility
- Supportive, collaborative work environment