Sr Data Scientist

HiLabs - Bethesda, MD

Full-time - Mid Level

Bethesda, MD

Publishing Industries

About the position

HiLabs is a leading provider of AI-powered solutions to clean dirty data, unlocking its hidden potential for healthcare transformation. HiLabs is committed to transforming the healthcare industry through innovation, collaboration, and a relentless focus on improving patient outcomes. As a Sr Data Scientist at HiLabs, you will partner closely with a team of stakeholders, product managers, and data engineers to solve real problems in the healthcare domain. HiLabs is looking for great problem solvers who can tap into the potential of data and deliver scalable and robust data products. Our potential member would work on developing and implementing machine learning algorithms for various HiLabs Products and Solutions.

Responsibilities

• Leveraging AI/ML techniques and solutions to identify and mathematically interpret complex healthcare problems.
• Deploy and optimize machine learning solutions on massive datasets using big data technologies.
• Develop and prototype AI algorithms and software tools.
• Implement enhancements to existing algorithmic/deep learning solutions.
• Conduct quantitative data analysis using a variety of datasets.
• Increase the efficiency and improve the quality of solutions offered.
• Understand business use cases and pull the necessary data from various sources to provide key insights to stakeholders. Build and deploy additional functionalities as per client requirements.
• Leverage recent advances in machine learning technologies and oversee the team's training.
• For Lead/Sr. Data Scientists-Lead a team of Data Scientists, developers as well as clinicians to strategize, design and evaluate AI based solutions to healthcare problems.

Requirements

• Bachelor's Degree/ master's degree in computer science, Mathematics, Statistics, Physics, Electrical Engineering, Computer Engineering or related fields from tier 1 college.
• 3-5 years of experience in data science with a focus on machine learning and predictive analytics.
• Experience or educational courses/projects in Machine Learning, and/or Text Mining Algorithms.
• Knowledge of statistical techniques, linear algebra, numerical optimization.
• Knowledge of ML theory such as bias variance tradeoff, regularization, loss functions and experienced in A/B testing.
• Know all the general best practices of Machine Learning.
• Ability to work closely with Domain experts to develop tools/algorithms needed to answer research questions in their studies.
• Excellent Communication Skills (with the ability to explain developed tools and ML algorithms to a non-technical audience).
• Ability to formulate operational problems in healthcare domain as technical problems that allows for reuse of leading research in the area.
• Proven ability to work independently to learn new technologies, techniques, processes, languages, platforms, systems.
• Strong analytical, inferential, critical thinking, and creative problem-solving skills.
• Self-starter with ability to work both independently and with a team.

Nice-to-haves

• Experience with LLM'S, Deep Learning Frameworks such as Keras, Tensorflow, PyTorch, Mxnet etc.
• Experience with interpretability of deep learning models.
• Big Data Skills (Hadoop, Spark, recent deep learning platforms).
• Experience with text mining tools and techniques including in areas of summarization, search (e.g. ELK Stack), entity extraction, training set generation (e.g. Snorkel) and anomaly detection.
• Expert software development skills including developing and maintaining production quality code.

Benefits

• Competitive Salary
• Accelerated Incentive Policies
• H1B sponsorship
• Comprehensive benefits package that includes ESOPs
• Financial contribution for your ongoing professional and personal development
• Medical coverage for you and your loved ones
• 401k
• PTOs
• Collaborative working environment
• Smart mentorship
• Highly qualified multidisciplinary professionals from highly renowned and accredited medical schools, business schools, and engineering institutes.