Data Scientist - AI / ML Product Strategy

Weekday AI; Cuneo; In loco; EUR 70.000 - 100.000; Tempo pieno

Descrizione del lavoro

A leading company seeks a visionary and technically skilled Data Scientist to lead AI/ML product development in business intelligence. This role emphasizes driving business impact through advanced machine learning and NLP techniques, requiring extensive experience in data science and product strategy.

Competenze

• Advanced degree in Data Science or related field.
• 8-12 years experience in machine learning and AI product development.
• 5+ years in product-facing data science roles.

Mansioni

• Define product direction for AI / ML-based platforms.
• Design and implement AI / ML models.
• Mentor a team of data scientists and support innovation.

Conoscenze

Python

R

SQL

NLP

Machine Learning

Formazione

MS / PhD in Data Science, Computer Science, Statistics

Strumenti

AWS

GCP

Azure

ML libraries and frameworks

Descrizione del lavoro

This role is for one of the Weekday's clients

Min Experience : 5 years

Location : Poland, Serbia, Bulgaria

JobType : full-time

We are seeking a visionary and technically skilled Data Scientist to lead the development and evolution of AI / ML-powered products within the business intelligence and analytics space. This role sits at the intersection of data science, product strategy, and innovation leadership, with a strong emphasis on driving measurable business impact through machine learning, NLP, and LLM capabilities.

Key Responsibilities : Strategic Product Leadership :

• Define and shape the product direction for AI / ML-based intelligence platforms.
• Align AI product roadmaps with market trends, business opportunities, and customer needs.
• Collaborate cross-functionally with engineering, data science, and GTM teams to execute product vision.
• Lead competitive analysis and positioning in the business intelligence domain.

Technical Development & Innovation :

• Design and implement advanced AI / ML models to solve real-world business problems.
• Build solutions leveraging LLMs, NLP, agent-based systems, and modern ML architectures.
• Work with tools such as vector databases, embedding-based search, and scalable ML systems.
• Apply best practices in data engineering, model training, evaluation, and deployment.
• Translate complex AI capabilities into customer-centric value propositions.
• Support product-market fit validation and client advisory engagements.
• Help demonstrate business impact and ROI across key verticals.
• Mentor and support a high-performing team of data scientists and product collaborators.
• Champion technical excellence and a culture of innovation.
• Lead knowledge-sharing initiatives and support upskilling across teams.

Business & Impact :

• Support revenue goals, adoption metrics, and product engagement outcomes.
• Contribute to launch planning and GTM efforts in collaboration with sales and marketing.
• Track performance metrics and ensure alignment with business growth objectives.

Required Qualifications : Technical :

• Advanced degree (MS / PhD) in Data Science, Computer Science, Statistics, or a related field.
• 8–12 years of experience in machine learning, data science, and AI product development.
• Strong programming skills in Python, R, SQL and familiarity with ML libraries and frameworks.
• Deep understanding of NLP, LLMs, and applied AI techniques.
• Experience with big data tools and cloud infrastructure (AWS, GCP, Azure).
• At least 5 years in product-facing data science roles, with 3+ years in senior or lead positions.
• Experience working in Business Intelligence, analytics, or AI-driven enterprise software.
• Demonstrated success in launching scalable technical solutions in B2B environments.

Business Acumen :

• Ability to collaborate with executive stakeholders and influence strategic decisions.
• Strong communication and data storytelling skills.
• Understanding of product monetization and customer-centric design.

Preferred Experience :

• Familiarity with vector databases, MLOps, or cloud-native ML infrastructure.
• Previous experience in high-growth startups or product scale-ups.
• Exposure to data privacy, governance, and compliance frameworks.
• Experience supporting enterprise sales and customer success functions.

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