Senior Data Scientist

at  Skillspark AB

REQUIREMENTS:

• Minimum 7+ years experience in a Senior capacity
  Mandatory Requirement: Honours Degree or Postgraduate Diploma (NFQ Level 8 or equivalent) in a Numerate Discipline (e.g. Computer Science, Data Analytics, Engineering, Economics, etc.)

Data Processing & Programming: Practical experience with data processing, SQL programming, and data analytics.

• Statistical Programming: Proficiency in statistical programming languages, such as R and Python, with familiarity in associated packages and libraries (e.g., Caret, Pandas, NumPy, Scikit-Learn).
• Data Mining & Analysis: Strong background in data mining and statistical analysis.
• Development Tools: Experience using Azure DevOps and Github for development processes.
• Dashboard Development: Proficiency in developing dashboards using PowerBI.
• GenAI & LLMs: Hands-on experience with Generative AI tools and Large Language Models.
• Data Transformation: Ability to understand various data structures and commonly used methodologies in data transformation.
• Stakeholder Communication: Skilled at explaining complex statistical concepts in a way that non-technical stakeholders can understand.
• Predictive Modelling & Business Strategy: Experience in statistical, mathematical, and predictive modelling, along with business strategy skills for building relevant algorithms.
• Product Knowledge: An understanding of product development processes.
• Data Privacy & Compliance: Knowledge of GDPR and the AI Act implications for data science applications.
• Systems Integration: Demonstrated experience in integrating multiple systems and data sets.

So, are you ready to join our team
It’s important to remember, emagine is an Equal Employment Opportunity Employer; we value bringing together individuals from diverse backgrounds to develop innovative solutions for our customers.
Opportunity type: Contract, 24 months, Hybrid Dublin
Laura Russell
Recruiter
laura.russell@emagine-consulting.ie
+35385801205

• Linking and integrating diverse data sets to uncover insights, including managing data from different sources and formats, with the capability to work with incomplete data sources.
• Cleaning data sets to ensure usability.
• Conducting descriptive and exploratory data analysis.
• Building and validating predictive and AI models.
• Implementing practical applications in Generative AI (GenAI) and, more broadly, with Large Language Models (LLMs).
• Developing dashboards to generate insights.
• Communicating analytical results to business stakeholders in a non-technical manner