Data Science Lead
Data Science Lead at Adaptfy, a SHV–Metyis joint venture delivering data & analytics services to SHV group companies, based in Amsterdam. Day to day: managing client relationships, translating business problems into analytics solutions, overseeing project delivery and mentoring junior data scientists, using SQL/BigQuery, Python/R, Tableau/PowerBI and machine learning.
- Compensation
- Not disclosed
- Location
- Amsterdam, Noord-Holland, Netherlands
- Work setup
- Hybrid
- Seniority
- Lead
- Employment
- Full time
- Experience
- 1+ years
- Category
- Data science
- Posted
Skills
Must have
- Master (or Ph.D.) degree in a quantitative field with focus on data analysis (e.g. econometrics, AI, mathematics, physics, other exact sciences)
- Sharp thinking ability to differentiate major contributors from irrelevant details and hierarchically structure information in data
- Robust knowledge of statistical concepts
- Expertise with analytical tools: databases (SQL, BigQuery, noSQL), programming languages (R, Python), data visualization (Tableau, PowerBI), and machine learning
- Proven ability to manage projects with expert team members and provide inspiring guidance to juniors
- 5-7 years of consulting or relevant industry experience, with at least 1+ years at project lead level
- Excellent communication skills, able to explain complex analytical concepts in non-technical terms
- Professional fluency in English
Nice to have
- Understanding of data engineering solutions
Full description
Adaptfy is growing! We are looking for a Data Science Lead with 5-7 years of experience for a full-time position to join our Data and Analytics team in Amsterdam.
Who we are.
Adaptfy is a turnkey data & analytics solution for the SHV family of companies. We support multiple SHV Groups with advanced Data & Analytics in all areas, elevating their operations in diverse industries across international markets and throughout entire value chains.
Adaptfy is a joint venture formed by SHV and Metyis. Metyis embodies visionary thinking in the digital world, while SHV is a globally established family of companies built on the values of integrity, trust, curiosity, passion, and inclusivity.
SHV has a presence across four continents, with businesses spanning energy, heavy lifting and transport, animal nutrition & aquaculture, testing, inspection & certification, and more. The organisation’s history reflects its courage to care—for people, for performance, and for future generations. Their success is a testament to constant reinvention through innovation.
At Adaptfy, we play a pivotal role in bringing SHV’s digital vision to life. From optimising supply chains across Europe to enhancing operational efficiency in South America, we connect the dots for SHV, shaping a data-driven future for the entire family of companies
What we offer
- Interact with senior stakeholders at our clients on regular basis to drive their business towards impactful change.
- Working with Data Scientists to take data throughout its lifecycle - acquisition, exploration, data cleaning, integration, analysis, interpretation, and visualization.
- Become part of a fast-growing international and diverse team.
What you will do
- Manage client interactions and play a key role in building and maintaining client relationships.
- Apply knowledge of data science concepts to translate business problems to analytics solutions with clear output and with foreseen impactful outcomes.
- Provide project management oversight required to meet client expectations.
- Responsibilities may include the development of presentations and managing of external relationships.
- Seek compelling opportunities beyond the scope of the current project
- Provide guidance to more junior members of the team, while orchestrating the work of the entire team.
What you will bring
- Master (or Ph.D.) degree in a quantitative field with a focus on data analysis (e.g. econometrics, AI, mathematics, physics, and other exact sciences).
- Sharp thinking ability to be able to quickly differentiate major contributors from irrelevant details and to hierarchically structure information residing in the data under analysis.