Data Scientist

SpotDraft is looking to automate the legal workflow from start to end. We are solving numerous Machine Learning and NLP problems, including classification, clustering, entity recognition and segmentation. We are looking for people who are good or want to become good at NLP and Machine Learning. 

We are looking for a Data Scientist who will support our product and leadership team. The ideal candidate is adept at using large data sets for product and process optimization and using models to test the effectiveness of different courses of action. They must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations. They must have a proven ability to drive business results with their data-based insights. 

We routinely evaluate and examine research papers that might help solve our problems. You might even get to author a paper based on the work done here.

What You'll Do:

• Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
• Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
• Creating custom ML models for analyzing legal contract
• Coordinate with different functional teams to implement models and monitor outcomes
• Develop processes and tools to monitor and analyze model performance and accuracy

What You'll Need:

• Good understanding of Python with basic libraries like numpy, pandas, matplotlib or any visualisation library
• Good understanding of at least one Deep learning or Machine Learning framework, like sklearn, tensorflow, keras or Pytorch
• Familiarity with Linux
• Strong problem-solving skills with an emphasis on product development
• Experience using statistical computer languages (Python, R, SQL, etc.) to manipulate/analyze large data sets
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, neural networks, etc.) and their real-world advantages/drawbacks
• Knowledge of advanced statistical techniques and concepts
• Experience querying databases and using statistical computer languages