Senior Data Scientist at Pipe Technologies
United States
$270k - $290k
<p><strong><span style="font-size: 10pt;">The Role</span></strong></p> <p>This is a full-time position as a Senior Data Scientist and this position may be located anywhere in the U.S. Design, develop and deploy machine learning and statistical models that forecast customer cash flows, credit risk and other measures of business health. Use experimentation and other statistical methods to test product, pricing and underwriting changes and to improve customer experience on the platform. Explore and analyze large datasets to identify relevant signals, engineer features and uncover insights that inform model and product design. Prototype and ship model driven features and data products that provide value to customers and internal stakeholders. Research and evaluate advanced deep learning architectures and training techniques, including transformer based and recurrent models, and implement innovations such as mixture of experts, semi supervised and generative approaches to improve core underwriting algorithms. Monitor models in production, investigate performance issues and retrain or update models as needed in collaboration with engineering, product and risk teams.</p> <p><strong><span style="font-size: 10pt;">Qualifications</span></strong></p> <ul> <li>3 years in the following: <ol> <li>Building and optimizing deep learning models for forecasting, classification and ranking that predict key user, product or business outcomes, including definition and improvement of model performance metrics such as accuracy, AUC, RMSE or MAPE.</li> <li>Designing, training and evaluating deep learning models for sequence and time series data, including transformer based architectures and recurrent neural networks, applied to forecasting or similar domains.</li> <li>Executing end to end machine learning projects, including data collection and preprocessing, feature engineering, model development, deployment to production systems and ongoing performance monitoring.</li> <li>Machine learning, deep learning, optimization, statistics and probability theory, including the design and analysis of loss functions, weight initialization schemes and neural network architectures under computational and data constraints.</li> <li>Statistical and causal inference methods, including probabilistic graphical models, Bayesian inference, difference in differences or propensity score based methods, to estimate the impact of business or product interventions.</li> <li>Experimentation, including design, execution and analysis of A/B tests and offline and online experiments in production environments.</li> <li>Large scale data processing and model training using modern machine learning frameworks such as PyTorch, TensorFlow, JAX, scikit learn, MXNet or Spark, and cloud platforms such as AWS or GCP.</li> </ol> </li> </ul> <p> </p> <p><strong><span style="font-size: 10pt;">Location</span></strong></p> <p>Position may work remotely from anywhere in the U.S. (HQ: San Francisco, CA)</p> <p> </p> <p><strong><span style="font-size: 10pt;">Compensation and Benefits</span></strong></p> <p><span style="font-size: 10pt;">We are a fully remote company and we believe in taking care of our employees. As a Pipe employee, you’ll receive:</span></p> <ul> <li><span style="font-size: 10pt;">The best equipment to help you do your job.</span></li> <li><span style="font-size: 10pt;">Flexible vacation and work hours. We believe in a healthy work-life balance (really!)</span></li> <li><span style="font-size: 10pt;">Excellent health, dental, and vision insurance.</span></li> <li><span style="font-size: 10pt;">Generous parental leave for anyone who is growing their family, regardless of gender.</span></li> <li><span style="font-size: 10pt;">Great colleagues! We value a culture of authenticity, humility, and excellence. We want you to make a mark on our culture.</span></li> </ul> <p><strong>Rate Of Pay</strong></p> <p>$270,000 to $290,000 per year. This salary range may be inclusive of several career levels at Pipe and will be narrowed during the interview process based on a number of factors, including the candidate’s experience, qualifications, and location.</p>
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