Job Title: Data Scientist Specialist

Job Overview

 

At Zurich, we rely on insightful data to power our systems and solutions. We’re seeking an experienced data scientist to deliver insights daily. The ideal candidate will have mathematical and statistical expertise, along with natural curiosity and a creative mind. While mining, interpreting, and cleaning our data, this person will be relied on to ask questions, connect the dots, and uncover hidden opportunities for realizing the data’s full potential. As part of a team of specialists, this position requires a strong blend of technical expertise, business acumen and a passion for using data and technology to solve problems.

 

Objectives of this role

  • Collaborate with product design and engineering teams to understand and address their needs.
  • Utilize statistical analysis, machine learning, and deep learning techniques to derive actionable insights and support data-driven decision-making.
  • Communicate findings and insights to stakeholders effectively.
  • Enable smarter and more efficient business processes through AI and automation.
  • Stay abreast of the latest advancements in data science, AI, and machine learning research, and apply them to solve real-world problems. 

Responsibilities

  • Assist in identifying and integrating new datasets that can be leveraged through our product capabilities, working closely with the engineering team.
  • Participate in end-to-end data science projects, including problem formulation, data exploration, model development, deployment, and monitoring.
  • Collaborate with cross-functional teams, including business stakeholders, data engineers, and software developers, to identify opportunities and deliver impactful solutions.
  • Support the development and utilization of algorithms and models to mine big-data stores; perform data and error analysis to improve models; clean and validate data for uniformity and accuracy.
  • Utilize Azure technologies like Azure Cognitive Services, Azure Machine Learning, and Azure Bot Services to assist in designing, creating, and deploying AI-based applications.
  • Contribute to the development and adoption of best practices in data science, including model governance, MLOps, and model interpretability.

 

Required skills and qualifications

  • One or more years of experience in data science with proven track record of executing multiple data science use cases. For fresh graduate candidates, at least one data science related internship experience.
  • Proficient in machine learning and deep learning with hands-on experience.
  • Solid understanding of data science concepts, including feature engineering, model evaluation, and performance optimization.
  • Knowledge of SQL and Python.
  • Experience using business intelligence tools (e.g., Power BI)
  • Experience working with cloud platforms is a bonus
  • Analytical mind and strong desire to learn
  • Strong math skills (e.g. statistics, algebra)
  • Problem-solving aptitude
  • Strong communication and interpersonal skills, with the ability to effectively collaborate with cross-functional teams and stakeholders.
  • Proven ability to work independently, prioritize tasks, and manage multiple projects simultaneously.
  • BSc/BA in Computer Science, Engineering or relevant field; graduate degree in Data Science or other quantitative field is preferred

 

You are the heart & soul of Zurich! 
 

At Zurich, we like to think outside the box and challenge the status quo. We take an optimistic approach by focusing on the positives and constantly asking What can go right? 

We highly value the experience and know-how of our employees and offer a wide range of opportunities across business areas to encourage you to apply for new opportunities within Zurich when you are ready for your next career step. 

Let’s continue to grow together!

 

  • Location(s):  MY - Kuala Lumpur 
  • Schedule: Full Time
  • Recruiter name: Eilma Fatehah Sabri

Location

Kuala Lumpur, MY

Job Overview
Job Posted:
2 months ago
Job Expires:
3w 5d
Job Type
Full Time

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