Senior Quantitative Machine Learning Lead

About Us

Auditoria is an AI-driven SaaS automation provider for corporate finance that automates back-office business processes involving tasks, analytics, and responses in Vendor Management, Accounts Payable, Accounts Receivable, and Planning. By leveraging natural language processing, artificial intelligence, and machine learning, Auditoria removes friction and repetition from mundane tasks while automating complex functions and providing real-time visibility into cash performance. Corporate finance and accounting teams use Auditoria to accelerate business value while minimizing heavy IT involvement, improving business resilience, lowering attrition, and accelerating business insights.

Founded in 2019 and backed by Venrock, Workday Ventures, Neotribe Ventures, Engineering Capital, and Firebolt Ventures, we build intelligent automation by combining fine-grained analytical orchestration of a company’s typical financial and audit workflows with conversational AI, delivering rapid value to the finance/audit back office.

In 2021, Auditoria earned industry recognition by being named to the Intelligent Apps Top 40 List, SSON’s Shared Services & Outsourcing Impact Awards, the Constellation Research ShortList for AI-Driven Cognitive Applications, HFS Research Hot Vendors, 2021 CRN Emerging Vendors List, TiE50 Award, and the winner of the inaugural Pitch Event by Constellation Research.

Core Responsibilities

Looking for strong candidates with a passion for participating in Auditoria’s mission. Areas of responsibilities:

Data Exploration and Model Development

• EDA with various source data to gain insight into data and clearly explain the observations. Formulate strategy of predictive models exploration and future product integration.
• Exploring and developing univariate and multivariate forecasting models (AR, ARIMA, VAR, etc.)
• Exploring and developing neural network-based forecasting models (examples include LSTM-based, AR-net, Neural Prophet, etc.)
• Systematic benchmarking of various models/platforms
• Oversee analytics dashboard design and implementation

Engineering

• Working with AWS services and product integration (EC2, S3, SageMaker, Forecast, Fraud Detection, etc.)
• Design/overseeing data pipeline (ETL) and data warehouse
• Integrating models from the research stage into the production stage (DevOps skills, shell scripting, etc.)

• Effectively analyze and resolve engineering issues as they arise
• Writing unit tests alongside the development team

Passionate about

• Attention to details and technology trends
• Commitment to customers’ requirements and timeline
• Dynamic startup work environment

Skillsets

Must-haves 

• Strong skills with EDA with Python or R.  Strong with foundational statistical analysis, common statistical distributions.  
• Proficient with linear space analysis, such as PCA, SVD, dimensional reductions, explainability, etc.
• Strong skills with time-series data processing, forecasting models. Traditional AR, MA, ARIMA, VAR. Familiar with neural network-based time series prediction/forecasting: gradient boosting, RNN, AR-net, etc.). 
• Must have strong programming skills in Python: data structures, OOP, scripting. 
• Familiarity with SQL database, data warehouse.
• Familiarity with Docker, Dockerfile.
• Familiarity with REST API, JSON structure. Python packages like FastAPI. 
• Familiarity with git operations
• Good communication skills with team members across different time zones.

Desired

• Strong statistical background, bayesian analysis, matrix exploration.
• Prior experience with price predictions, cash flow forecasting, etc.
• Familiarity with AWS Forecast, Fraud Detection
• Understanding of neural networks underpinnings and toolkits like Pytorch or Tensorflow
• Familiarity with traditional accounting methodology for quantitative forecasting
• Prior startup work experience desired. 

Additional Information