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Data Scientist

Employer
Wood Mackenzie
Location
Louisville, Kentucky, US
Salary
Competitive
Closing date
Oct 16, 2021

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Company Description

Wood Mackenzie is the global leader in data, analysis and consulting across the energy, chemicals, metals, mining, power and renewables sectors.

Founded in 1973, our success has always been underpinned by the simple principle of providing trusted research and advice that makes a difference to our customers. Today we have over 2,000 customers ranging from the largest global energy companies and financial institutions to governments as well as smaller market specialists.

Our teams are located around the world. This enables us to stay closely connected with customers and the markets and sectors we cover. Collectively this allows us to offer a compelling combination of global commodity analysis with detailed local market knowledge.

We are committed to supporting our people to grow and thrive. We value different perspectives and aspire to create an inclusive environment that encourages diversity and fosters a sense of belonging. We are committed to creating a workplace that works for you and encourage everyone to get involved in our Wellness, Diversity and Inclusion, and Community Engagement initiatives. We actively support flexible working and are happy to consider alternative work patterns, taking into account your needs and the needs of the team or division that you are looking to join.

Hear what our team has to say about working with us:

https://www.woodmac.com/careers/our-people/

We are proud to be a part of the Verisk family of companies!

At the heart of what we do is help clients manage risk. Verisk (Nasdaq: VRSK) provides data and insights to our customers in insurance, energy and the financial services markets so they can make faster and more informed decisions.

Our global team uses AI, machine learning, automation, and other emerging technologies to collect and analyze billions of records. We provide advanced decision-support to prevent credit, lending, and cyber risks. In addition, we monitor and advise companies on complex global matters such as climate change, catastrophes, and geopolitical issues.

But why we do our work is what sets us apart. It stems from a commitment to making the world better, safer and stronger.

It's the reason Verisk is part of the UN Global Compact sustainability initiative. It's why we made a commitment to balancing 100 percent of our carbon emissions. It's the aim of our "returnship" program for experienced professionals rejoining the workforce after time away. And, it's what drives our annual Innovation Day, where we identify our next first-to-market innovations to solve our customers' problems.

At its core, Verisk uses data to minimize risk and maximize value. But far bigger, is why we do what we do.

At Verisk you can build an exciting career with meaningful work; create positive and lasting impact on business; and find the support, coaching, and training you need to advance your career. We have received the Great Place to Work® Certification for the fifth consecutive year. We've been recognized by Forbes as a World's Best Employer and a Best Employer for Women, testaments to our culture of engagement and the value we place on an inclusive and diverse workforce. Verisk's Statement on Racial Equity and Diversity supports our commitment to these values and affecting positive and lasting change in the communities where we live and work.

Job Description

Wood Mackenzie owns and operates a growing international network of 5000+ sensors collecting real-time data on commodity sectors: energy, metals/mining, shipping, and manufacturing. Wood Mackenzie's proprietary monitoring systems measure millions of data points per day.

The Data Scientist will work with a team of cross-functional experts to enhance Wood Mackenzie's monitoring, ingestion, analysis, and publication of renewable and energy transition data. The role holder will be responsible for advancing data and image processing to convert into information. The data flows through the entire life cycle, originating at the monitoring source through ingestion, manipulation, storage, and delivery.

Key Responsibilities
  • Keep the focus on a globalized product, eliminating region-specific solutions
  • Collaborate with research, scientist, and field operations to develop an understanding of needs
  • Develop custom data models and algorithms to apply to raw data to convert into actionable information.
  • Use predictive modeling or machine learning to increase and optimize customer experiences, revenue generation, ad targeting, and other business outcomes.
  • Develop processes and tools to monitor and analyze model performance and data accuracy.
  • Communicate analytic solutions to stakeholders and implement improvements as needed to operational systems
Qualifications
  • BS in Statistics, Mathematics, Information Technology or another quantitative field
  • Python, SQL, or other programming experience preferred
  • Knowledge of mathematical foundations and statistical analysis.
  • Knowledge of various machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
  • 3+ years direct analyst or scientist experience
  • You are committed to contributing to a welcoming and inclusive environment, are sensitive to diverse perspectives, and are passionate about equity in the workplace.
Additional Information

Verisk Analytics is an equal opportunity employer.

All members of the Verisk Analytics family of companies are equal opportunity employers. We consider all qualified applicants for employment without regard to race, religion, color, national origin, citizenship, sex, gender identity and/or expression, sexual orientation, veteran's status, age or disability.

http://www.verisk.com/careers.html

Unsolicited resumes sent to Verisk, including unsolicited resumes sent to a Verisk business mailing address, fax machine or email address, or directly to Verisk employees, will be considered Verisk property. Verisk will NOT pay a fee for any placement resulting from the receipt of an unsolicited resume.

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