Data Scientist

Data Scientist

Requirements

• 5+ years of practical machine learning experience
• Experience in building and optimizing supervised and unsupervised machine learning models including deep learning and various other modern data science techniques
• A fundamental understanding of mathematical and machine learning concepts such as calculus, back propagation, ReLU, Bayes’ theorem, Random Forests, time series analysis, and more
• Proficient in SQL, Python, PySpark, PyTorch, PyTorch Lightning, Scikit, Distributed Data Parallel, Distributed Model Parallel
• Strong communication skills, including to non-technical audiences
• Visa sponsorship is not available for this posting. Applicants must be authorized to work for any employer in the US
• Bachelor’s in mathematics, statistics, computer science, or a similar field

Responsibilities

• Consult closely with colleagues to understand their needs
• Research and determine the appropriate analytics based on the data and the desired outcomes, using techniques including feature detection, statistics, data mining, predictive modeling, machine learning, natural language processing, and business intelligence
• Perform feature engineering, model selection, and hyperparameter optimization to yield high predictive accuracy and deploy the model to production in a cloud
• Write custom programs using supervised and unsupervised algorithms to build machine-learning models that optimize outcomes and deliver refined results
• Develop processes and tools to monitor and analyze model performance and data accuracy. Mine data using advanced techniques such as Regression, Clustering, Random Forest, Text Mining, NLP, and Neural Networks to provide in-depth insights from data

Preferred Qualifications

• Familiarity with one or more of the following: Git, Pytorch, Tensorflow, CUDA/GPU programming
• Experience handling massive batch/streaming data using big data tools and storage, such as Apache Spark
• Experience interacting with AWS, Azure, Fabric, or other cloud service
• Experience building reinforcement learning models