Data Scientist - Accounting Specialty

Job Description

Job Description Salary: 70,000 - 80,000
About Uplinq
We are an Arizona-based startup building software to automate bookkeeping and accounting for small businesses. This wasn’t always our plan. When we started Uplinq, we wanted to help small businesses work with

acquirers/investors,

survive loan due diligence, and use their financial data to make timely business decisions. We tried solving these problems directly to no avail and quickly realized they are all symptoms of a bigger problem: bookkeeping is hard, and SMBs hate doing it. Since then we’ve been hard at work reinventing bookkeeping as a SaaS product. What we’ve built to date allows us to offer high-quality, real-time, and affordable bookkeeping to hundreds of small businesses.

Uplinq is looking for a Data Scientist to join its core team. A Data Scientist in this role will work closely with both CPAs and engineers to solve high-impact business problems by directly communicating with stakeholders, creating models and software to analyze financial data, and monitoring their work for impact once deployed.

Requirements:
Minimum of 24 months of experience using Data Science methodologies to analyze financial datasets •
Minimum of 24 months of experience with the Python programming language •
Minimum of 24 months of experience with Python libraries for Data Science such as Numpy, Pandas, and Scipy •
Experience with small business financial modeling a plus.

Responsibilities:
Data Analysis & Modeling: Use data science techniques including data mining, machine learning, and natural language processing to extract and interpret meaningful insights from large, structured and unstructured datasets. This includes datasets pertaining to cash and accrual accounting, as well as other financial activities.
Accounting Principles Integration: Apply knowledge of accounting principles, specifically cash and accrual accounting, to enhance data models and analysis processes. Ensure that these models accurately reflect financial realities and facilitate reliable predictions based on both cash and accrual-based systems.
Development of Analytics Applications: Develop analytics applications using data-oriented programming languages to transform raw financial data into actionable insights. These applications must be capable of handling data from accounting systems, integrating this information in a meaningful way to aid in business decision-making.
KPI Analysis and Reporting: Identify, analyze, and report on key performance indicators (KPIs) in the realm of finance and accounting. Develop data models that can predict KPI trends and provide strategic recommendations to the business.
Visualization of Accounting and Financial Data: Use data visualization tools to design and create dynamic data reports and dashboards. These should effectively communicate complex information regarding accounting as well as financial KPIs to stakeholders.
Trend Analysis: Apply trend analysis to complex financial datasets, which include accounting data as well as financial KPIs. This analysis should contribute to strategic decision-making, risk management, and financial forecasting.
Cross-Functional Collaboration: Collaborate with accounting and finance teams to better understand financial parameters, including the nuances of cash and accrual accounting as well as relevant KPIs. Use this understanding to refine data analysis and reporting techniques.

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