Data Intelligence - Data Scientist

Ampcus Inc.•Washington, DC

13h•Onsite

About The Position

Ampcus Inc. is seeking a highly motivated Data Scientist to join their talented team. This role involves developing algorithms, writing scripts, building predictive analytics, and applying machine learning techniques to extract valuable insights from data. The goal is to help senior leadership make informed decisions by providing a deep understanding of the data and its implications. The position focuses on discovering hidden information within large datasets to improve products and deliver smarter solutions. Responsibilities include applying data mining techniques, performing statistical analysis, and building high-quality prediction systems. A key aspect of this role is the development and maintenance of new and existing ML models, as well as developing Generative AI solutions using platforms like AWS SageMaker, AWS Bedrock, and AWS AgentCore. The role also involves selecting features, optimizing classifiers, and enhancing data collection procedures. Processing, cleansing, and verifying data integrity, conducting ad-hoc analysis, and presenting results clearly are also part of the responsibilities. Additionally, the role includes creating automated anomaly detection systems and monitoring their performance.

Requirements

Master's Degree or Ph.D. in STEM with 2 years preferred OR Bachelor’s Degree in STEM with 7 years.
Experience in predictive modeling, data science, machine learning, or user and entity behavior analytic development.
Experience in developing predictive, prescriptive, optimization, and forecasting models, including the use of contemporary techniques such as support vector machines, neural networks, and gradient boosting.
Experience in developing Generative AI solutions with LLM and custom models.
Experience in interpreting results from statistical and mathematical models.
A minimum of two years’ experience in applying Data Science/ Analytics to Health Insurance/Health Care related problems/use cases.
At least 1 year focused on applications of Generative AI solutions.
Experience in programming languages like Java, Python and/or R for complex data manipulation, statistical analysis, and machine learning.
Experience in Analytic development with streaming and a Hadoop ecosystem (Hive, Spark, etc.).
Experience in SQL, NoSQL, graph-based, Key/Value stores, document stores.
Experience with cloud-based data platforms like Snowflake and using them to train/build models.
Experience with one or more cloud services (AWS SageMaker, MS Azure).
Experience applying machine learning to real-world production systems, analytic development based on SparkML and other ML libraries.
Comfort with complex mathematical concepts and models.
Experience with Agile Methodology/Scrum Development and DevOps.
Ability to work in a fast-paced environment and shift gears quickly.

Nice To Haves

Additional Skills: AWS SageMaker/ AI & ML / Model development / Gen AI development.
Experience in advanced data visualizations and interpretation using data visualization tools (e.g., Tableau, Power BI).
Familiarity with common Linux/Unix command line tasks and version control software like git or svn.
History of solving difficult problems using a scientific approach.

Responsibilities

Develop algorithms, write scripts, build predictive analytics, use automation, and apply machine learning.
Use the right combination of tools and frameworks to turn data points into objective answers for senior leadership.
Provide the team with a deep understanding of their data, its meaning, and how to use it.
Discover information hidden in vast amounts of data to help make smarter decisions and deliver better products.
Apply data mining techniques, perform statistical analysis, and build high-quality prediction systems integrated with products.
Develop new ML models and maintain existing ones and their processes.
Develop Gen AI solutions using AI platforms like AWS SageMaker, AWS Bedrock, AWS AgentCore.
Select features, build and optimize classifiers using machine learning techniques, and data mining using state-of-the-art methods.
Enhance data collection procedures to include information relevant for building analytic systems.
Process, cleanse, and verify the integrity of data used for analysis.
Perform ad-hoc analysis and present results in a clear manner.
Create automated anomaly detection systems and constantly track their performance.
Use and develop statistical analysis, modeling, simulation, and machine learning methods.
Communicate and present work effectively and influence others.