QuoteWizard by LendingTree’s Data Science team is searching for an Applied Data Scientist that is hardworking, and passionate for all things data, and scalable infrastructure. We are looking for someone who is a self-starter but wants to work collaboratively with other members of the team, business, marketing, and engineering. In this role you will get exposure to all aspects of the organization, hungry to learn more from your insights. You should work without constraints of reality and build truly amazing products. Data Science at LendingTree is all about helping our consumers, and we want you, as a member of the Data Science team, to strive to be an advocate and thought-provoking contributor of Data Science at LendingTree.
- Collaborate with software developers and product managers to plan and construct the architecture surrounding model deployment.
- Perform and implement model assessments, validation, and enhancement activities.
- Works with the business teams to set a clear vision and direction for experiments and models.
- Design and implement creative approaches to optimization and predictive modeling problems.
- Maintain a working knowledge of data mining and visualization best practices.
- Acquire any specialized domain knowledge required to be more effective in all required activities
- 3+ years of experience in product-oriented quantitative analysis and Data Science.
- Masters or PhD degree in Computer Science, Math, Statistics, or STEM field.
- Strong skills in Python and SQL.
- Experience with scikit-learn and Azure Machine Learning Service ecosystems.
- Able to use Docker with Kubernetes in a Continuous Delivery pipeline for applications.
- Familiarity with writing queries and working with databases (SQL, Hbase, Hive).
- Extensive hands-on experience working with large data sets, including statistical analyses, data visualization, data mining, and data cleansing/transformation.
- Knowledge of machine learning concepts: supervised/unsupervised learning, loss functions, regularization, feature selection, regression/classification, cross-validation, bagging, kernel methods, sampling, and probability distributions.
- Experience prototyping and deploying Data Science solutions.
- Strong ability to communicate complex analytical results in forms that resonate with scientific and/or business collaborators, highlighting actionable insights.
- Entrepreneurial inclination to discover novel opportunities for applying analytical techniques to business/scientific problems across the company.
- Understand underlying scientific experimental setup, process, and analysis and to facilitate effective communication.
Our office is located in downtown Seattle, in the heart of Pioneer Square. We cultivate a comfortable work environment, with plenty of onsite amenities (gym, locker room, lunch area, snacks, TVs, casual attire, ping pong, etc.). Come join a diverse and growing workforce of over 150 smart, driven people.
What you should know about LendingTree, our parent company:
- We’re a publicly-traded company (TREE).
- We’ve welcomed several other companies into the LendingTree family to augment our efforts at helping borrowers make their most sensible financial choices.
- We’ve built the LendingTree app and My LendingTree dashboard to give consumers tools to manage and monitor their financial health.
- We still make funny commercials.
We’re a fast-paced company with an entrepreneurial bend. We work hard and test our products often. We’re collaborative, ambitious, candid and high-energy. Our teammates are some of the brightest, most talented people you’ll ever work with. We care more about your smarts than we do about the kinds of clothes you wear (but please, do wear clothes to work!), and we’re pretty good about rewarding innovation, creativity and the knack for just getting stuff done (we even have an award for employees called the GSD, “Get Stuff Done”).
Come work with us!
QuoteWizard by LendingTree is the kind of company that not only promotes diversity and inclusion; we thrive because of these values. We do not discriminate based on race, color, religion (or creed), gender, gender expression, age, national origin, disability, marital status, sexual orientation or military status.