Responsibilities:

• Design, develop, and implement NLP algorithms and techniques for text preprocessing, feature extraction, sentiment analysis, topic modeling, named entity recognition, document classification, and other related tasks.
• Develop robust classification models and frameworks using state-of-the-art machine learning and deep learning techniques for various applications, including document categorization, text classification, sentiment analysis, and recommendation systems.
• Help define workflow and data stores to get data out of unstructured stores and into usable Data Science formats. 
• Collaborate with cross-functional teams, including product owners, software developers, and domain experts, to understand business requirements and develop end-to-end solutions.
• Perform exploratory data analysis and visualization to gain insights into textual data, identify patterns, and inform feature engineering and model development.
• Evaluate and compare the performance of different (but not limited to) NLP, generative and classification models, and propose enhancements or modifications to improve their accuracy, efficiency, and scalability.
• Stay up-to-date with the latest advancements in machine learning methodologies, techniques, and frameworks, and apply them to solve complex business problems.
• Communicate findings, insights, and technical concepts effectively to both technical and non-technical stakeholders through reports, presentations, and visualizations.
• Support implementation of analytics tools and methodologies within our engineering tech stack.  

Requirements:

• Master’s or Ph.D. degree in Computer Science, Data Science, Statistics, or a related field.
• Strong background and expertise in Natural Language Processing (NLP) techniques, including text preprocessing, feature extraction, sentiment analysis, topic modeling, named entity recognition, and document classification.
• Proven experience in designing and implementing classification models and algorithms, such as Naïve Bayes, Logistic Regression, Support Vector Machines (SVM), Random Forests, Gradient Boosting, and Neural Networks.
• Proficiency in programming languages such as Python, Spark or Java, and libraries/frameworks such as NLTK, SpaCy, scikit-learn, TensorFlow, or PyTorch.
• Experience with data manipulation, analysis, and visualization using tools such as Pandas, NumPy, Matplotlib.
• Strong understanding of statistical analysis and machine learning principles, and ability to apply them to real-world legal problems.
• Solid knowledge of software development practices, version control systems, and agile methodologies.
• Excellent problem-solving skills, analytical thinking, and attention to detail.
• Effective communication skills and ability to collaborate in a team-oriented environment.
• Proven track record of delivering high-quality results on time and effectively managing high profile projects and priorities.

Preferred Skills:

• Experience with true big data (exabytes and higher) procession practices.
• Knowledge of cloud computing platforms such as AWS, Azure, or GCP.
• Ability to mentor and educate on Data Science deployment and best practices to technical groups