Data Scientist

Millions of people are seeking to improve their lives through home ownership and intelligent use of lending products like mortgages - however, the entire process is often complex, unclear and boils down to choosing the lowest rate and monthly payment. Unfortunately, most lenders are not providing the education and guidance to their existing or potential borrowers as they should.

We are transforming this dynamic! Hundreds of top lenders depend on the award-winning technologies from TrustEngine to build enduring borrower relationships that optimize lifetime customer value for the lender and empower the borrower. We are reinventing the lending and financial sector by linking borrowers with the right loan at the right time. Our team is constantly in search of passionate and diligent members who can help lift us to new heights in assisting borrowers to achieve their objectives.

About the Role:

If you are fueled by enthusiasm for data, thrive on driving change using data and aspire to go above and beyond, we want to hear from you.

We are hiring a skilled and motivated individual to join our team as our first Data Scientist. This is an exciting opportunity to shape the future of TrustEngine by directing data-driven decision-making processes through the creation of advanced machine learning models that push the boundaries of what's achievable in the fintech field, all aimed at contributing towards our overarching mission of enabling every homeowner to achieve financial independence.

You’ll be our sole data scientist, but you’ll be part of a team that is passionate about effecting change with data and perpetually achieving new knowledge and quality levels to support the business as a whole. Additionally, you’ll be reporting to the Director of Data, who is skilled in data science & analytics, and is enthusiastic about scaling data teams by developing each team member individually and collectively.

We operate rapidly and agilely, so flexibility and an iterative mindset are key for this role. Move swiftly, be imperfect, but be honest about the imperfection as we improve. Your data proficiency will be utilized to articulate the value our business provides and you’ll consistently have an opportunity to outdo yourself.

As our first Data Scientist, your responsibilities will include:

Data Analysis and Exploration:

Gather, clean, and process data from multiple sources to form derivative datasets.

Carry out exploratory data analysis to discover trends, patterns, and insights that can influence business decisions. Partner with product and business team leads to constantly display results, gather feedback and iterate.

Model Development:

Create predictive and machine learning models to address specific business problems, such as customer segmentation, scoring models, identifying high-performance creatives or recommendation systems. Write code to effectively handle and combine data sources in innovative and helpful ways, frequently producing analytics datasets that are easily used by the wider team. Partner with engineering to put these datasets into production.

Data Visualization:

Design informative data visualizations and reports to share findings and insights with non-technical stakeholders. Transform data discoveries into compelling narratives and communicate them.

Collaboration:

Work with cross-functional teams, including Customer Success, Sales, Marketing and Leadership, to pinpoint data-driven opportunities and solutions. Act as a subject matter expert on data-related topics and provide guidance to the team.

Continuous Learning:

Stay current with the latest breakthroughs in data science, machine learning, and fintech industry trends. Test out new methods and tools to increase the effectiveness of data-driven initiatives.

Why join TrustEngine?

As a fully remote organization, we devise inventive and efficient ways to stay connected with each other while giving employees the freedom to manage their workload in a way that suits their lifestyle. Whether you need time off to spend with your family or to attend a doctor's appointment, we don't micromanage your schedule as long as work is completed and goals are met.

We uphold an open culture and encourage honest feedback from our employees. Managers meet with their teams on a one-on-one basis each week to review projects and discuss any concerns or obstacles. Both parties are encouraged to share their thoughts.

Requirements

Education:

• Bachelor's degree in a related field such as Computer Science, Statistics, Mathematics, Physics, Engineering, or another quantitative discipline. A Master’s degree would be considered an advantage.

Technical Skills

• Strong proficiency in programming languages is vital. Should be skilled in languages such as Python or R, which are commonly used for data manipulation, analysis, and machine learning.
• Competence in data manipulation libraries (e.g., Pandas for Python, dplyr for R) and statistical analysis tools are critical.
• Understanding of machine learning algorithms and strategies, including supervised and unsupervised learning, regression, classification, clustering, and deep learning.
• Expertise in data visualization tools such as Matplotlib, Seaborn, ggplot2, or Tableau to create significant visualizations.
• Familiarity with databases and SQL for data extraction and manipulation, including knowledge of both relational and NoSQL databases.
• Knowledge of big data technologies such as Hadoop, Spark, or NoSQL databases like MongoDB would be an advantage.
• Proficiency with version control systems like Git to collaborate on code with other team members.

Soft Skills:

• Analytical Thinking: The ability to analyze intricate problems, decompose them into manageable parts, and develop data-driven solutions.
• Problem-Solving: Strong problem-solving skills are crucial to identify and tackle challenges within data and models.
• Communication: Effective communication skills, including the ability to translate complex findings into simple language for stakeholders.
• Teamwork: The ability to work effectively with colleagues from different backgrounds and collaborate on problem-solving is important.
• Continuous Learning: Keeping up-to-date with the latest trends and techniques in the rapidly evolving field of data science is crucial.
• Business Acumen: Understanding business dynamics and aligning data-driven insights with organizational objectives is crucial.

Benefits