Data Scientist

Ennoventure is a venturebacked deeptech company with revolutionary technology to detect counterfeit goods. Our vision is to create a trusted value chain for delivering authentic products and experiences. We offer a fastpaced and highly collaborative work environment with significant opportunities for professional growth.

As a Data Scientist you will be responsible for implementing advanced AI/ML algorithms with focus on Computer Vision building and maintaining data pipelines and managing data science projects. You will work closely with ML Engineers to bring models into production and ensure datadriven insights align with business goals.

What will you do

Lead the application and integration of cuttingedge AI/ML algorithms with focus on Computer Vision into our product offerings ensuring the highest levels of accuracy and efficiency.

Design develop and optimize data pipelines to support seamless data flow and processing ensuring data integrity and availability for advanced analytics.

Take full responsibility for managing and delivering data science projects from initial concept to deployment ensuring alignment with business goals and timelines.

Work closely with machine learning engineers to transition data science models into scalable and reliable production systems.

Analyzing large datasets to extract insights identify trends and make datadriven decisions that support business strategies.

Staying updated with the latest advancements in computer vision and machine learning to continually enhance the effectiveness of algorithms and models.

Clearly communicating findings and recommendations to stakeholders including nontechnical team members to guide decisionmaking processes

Requirements Ph.D. or Master s degree in Computer Science Data Science Statistics Mathematics or a related field.

Minimum of 4 years of handson work experience in computer vision and machine learning roles.

Proven track record in implementing AI/ML algorithms and deploying them in a production environment.

Experience in building and maintaining complex data pipelines.

Strong programming skills in Python and familiarity with ML libraries (e.g. TensorFlow PyTorch).

Deep understanding of machine learning algorithms data processing techniques and statistical methods.

Expertise in big data technologies and cloud platforms (e.g. AWS Google Cloud).

Excellent problemsolving abilities and a strategic mindset.

Experience in collaborating with crossfunctional teams including engineers and product managers.

Strong communication skills with the ability to explain complex concepts to nontechnical stakeholders.

Benefits We believe that our people are critical for our ambitious growth plans. We want to build an organization where people thrive feel included grow professionally and enjoy having high impact through their work.

We follow a Total rewards philosophy incorporating all elements of pay performance rewards wealth creation wellbeing learning development and career: Pay: Competitive Salary Financial Reward: Performancebased Wellbeing: Health insurance & Mental health programs Learning: Ongoing investment in you and your skills. Specific selfdevelopment interventions based on your performance and potential. Compensation reviews based on role value determined by mapping and the positioning in the Market

Minimum of 4+ years of hands-on work experience in computer vision and machine learning roles. Proven track record in implementing AI/ML algorithms and deploying them in a production environment. Experience in building and maintaining complex data pipelines. Strong programming skills in Python and familiarity with ML libraries (e.g., TensorFlow, PyTorch). Deep understanding of machine learning algorithms, data processing techniques, and statistical methods. Expertise in big data technologies and cloud platforms (e.g., AWS, Google Cloud). Excellent problem-solving abilities and a strategic mindset. Experience in collaborating with cross-functional teams, including engineers and product managers. Strong communication skills, with the ability to explain complex concepts to non-technical stakeholders

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