Sr. Data Scientist (Volunteering -Unpaid)

Key Responsibilities:

• Collecting data through means such as analyzing business results or by setting up and managing new studies.

• Transferring data into a new format to make it more appropriate for analysis.

• Creating new, experimental frameworks to collect data.

• Building tools to automate data collection.

• Searching through large data sets for usable information.

• Creating reports and presentations for business uses.

• Correlating similar data to find actionable results.

• Perform statistical analysis of results and refine models.

Qualifications:

• A Master's or advanced degree in Data Science, Computer Science, Statistics, or a related field.
• A minimum of 5 years of experience in data exploration, data cleaning, data analysis, data visualization, and data mining.
• Mandatory to have 6months -1 yr of LLM experience in projects.
• Deep understanding of Machine Learning & Deep Learning algorithms
• Knowledge of most of the following quantitative fields: Natural Language Processing, Information Retrieval, Machine Comprehension, Question Answering/Conversational AI, Reinforcement Learning, Knowledge Graph, Causal Inference, and Design of Experiment
• Statistical and data science programming skills, such as Python and R
• SQL expertise for data extraction, transformation, and analysis
• Experience in Big Data, search, NLP, and chatbot technologies such as Elasticsearch and Solr
• Knowledge of Deep Learning and related tools such as TensorFlow, Keras, MXNET, or H2O is preferred
• Experience deploying NLP solutions in a commercial environment.
• Extensive experience with Microsoft Azure, including certification in machine learning
• Experience with machine learning pipelines (Azure ML)
• Experience in an Agile working environment and related project management tools (Jira, Azure DevOps, etc.)
• Public AI-related projects you have developed available on GitHub

What You'll Do:

• Work closely with software and business teams to build, integrate and deliver successful algorithms in a modern SaaS platform.
• Contribute to advancing NLP and its applications, to create highly innovative products and customer experiences, ranging from text understanding and classification to information extraction.
• Build end-to-end products from the ground up, from data exploration through feature generation and model construction and deployment.
• Own design, implementation, and deployment of your models and see them deliver business impact.
• Mentor other team members and continuously further the Data Science organization.
• Guide design, architecture, tooling, and processes at the organization level.