Data Scientist

What will you do?

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  Take full ownership of managing and delivering data science projects, from initial concept to deployment, ensuring alignment with business goals and timelines.
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  Design, develop and optimize AI/ML models (e.g., object detection, segmentation, and synthetic data generation) using frameworks such as PyTorch and TensorFlow.
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  Collaborate with ML Engineers to integrate models into production systems on cloud platforms (AWS, GCP, Azure)
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  Build and maintain robust data pipelines, ensuring data quality and repeatability. Evaluate model performance using relevant performance metrics to achieve target performance.
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  Analyzing large datasets to extract insights, identify trends, and make data-driven decisions that support business strategies.
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  Document research findings and present the project updates to cross-functional teams, to guide decision-making process.
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  Stay updated with the latest advancements in computer vision and machine learning to enhance the effectiveness of algorithms and models.

Requirements

What do we look for at Ennoventure?

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  Ph.D. or Master’s degree in Computer Science , Data Science, Statistics, Mathematics, or a related field
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  Minimum of 4+ years of hands-on work experience in computer vision and machine learning roles.
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  Proven track record of implementing AI/ML algorithms and deploying them in a production environment.
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  Experience in building and maintaining complex data pipelines for large datasets
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  Strong programming skills in Python and familiarity with ML libraries (e.g., TensorFlow, PyTorch).
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  Deep understanding of machine learning algorithms, data processing techniques, and statistical methods.
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  Expertise in big data technologies and cloud platforms (e.g., AWS, Google Cloud).
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  Excellent problem-solving abilities, strategic thinking, and attention to details.
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  Demonstrate ability to collaborate effectively with cross-functional teams, including engineers and product managers.
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  Strong written and verbal communication skills; ability to convey complex technical concepts clearly to nontechnical stakeholders.

Good to have:

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  Peer- reviewed publications in top-tier conference or journals
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  Experience with real-time systems or edge-device deployments
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  Familiarity with MLOps best practices (CI/CD, Jenkins, Docker)