Data Scientist

JOB DESCRIPTION

• Processing, cleansing, and verifying the integrity of data used for analysis.
• Doing ad-hoc analysis and presenting results in a clear manner.
• Analyze large, complex datasets to extract insights and decide on the appropriate technique.
• Selecting features, building and optimizing classifiers using machine learning techniques.
• Data mining using state-of-the-art methods.
• Extending customers data with third party sources of information when needed.
• Enhancing data collection procedures to include information that is relevant for building analytic systems.
• Use data modelling and evaluation strategy to find patterns and predict unseen instances.
• Read and follow the Underwriters Laboratories Code of Conduct and follow all physical and digital security practices.
• Performs other duties as directed.

RESPONSIBILITIES

• Mission: For UL, corporate and social responsibility isn't new. Making the world a safer, more secure, and sustainable place has been our business model for over 125 years and is deeply ingrained in everything we do.
• People: Ask any UL employee what they love most about working here, and you'll almost always hear, "the people." Going beyond what is possible is the standard at UL. We're able to deliver the best because we employ the best.
• Interesting work: Every day is different for us here as we eagerly anticipate the next innovation that our customers create. We're inspired to take on the challenge that will transform how people live, work, and play. And as a global company, in many roles, you will get international experience working with colleagues around the world.
• Grow & achieve: We learn, work, and grow together with targeted development, reward, and recognition programs as well as our very own UL University that offers extensive training programs for employees at all stages, including a technical training track for applicable roles.

QUALIFICATIONS

• Minimum of bachelor's degree in Data Analytics, Data Science, Computer Science or any other related field of science.
• Strong problem-solving skills with an emphasis on product development.
• Experience querying databases and using statistical computer languages.
• Experience using statistical computer languages to manipulate data and draw insights from large data sets.
• Experience with a variety of machine learning techniques and their real-world advantages/drawbacks.
• Detailed knowledge of machine learning evaluation metrics and best practice.
• Strong Python coding skills and problem-solving, good analytical & communication skills.
• Experience of working in an Agile environment for applied research.
• Knowledge of machine learning engineering end-to-end delivery process.
• Experience with data visualization tools and techniques.
• Demonstrated effective interpersonal, influencing, collaboration and listening skills.
• Ability to articulate an idea with strong communication and presentation skills.
• Work as part of a small team, sharing ideas and contributing to the strategic goals.

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