Data Scientist

Capgemini•Washington, DC

6d•$100,000 - $120,000•Onsite

About The Position

Capgemini Government Solutions (CGS) LLC is seeking a highly motivated Data Scientist to develop and deploy models and methodologies that enable pattern analysis, predictive modeling, intelligent automation, and AI/ML solutions. The successful candidate will have the opportunity to conceive and deliver innovative data science solutions to the U.S. Public Sector to help address critical national and global challenges such as health, climate change, and national security. This individual will join our Data and AI practice in the DC Metro Area. As a part of the rapidly expanding CGS Data and AI practice, the candidate will also help our clients develop, deploy, and modernize their data science products and ecosystems. At Capgemini, we are committed to our staff’s professional development and offer a wide range of training and educational resources. In addition to our internal learning sites, we partnered with Coursera and Degreed to offer our staff the latest courses from academic institutions around the world. We provide education expense reimbursements as well as sponsor seminars, conferences, and certifications. Our practice leaders work with every team member to chart appropriate career paths and goals to ensure that we all stay innovative and transformative, which maximizes our ability to scale up our solutions, keep up with the cutting edge, and bring the art of what’s possible to the Federal Government.

Requirements

U.S. Citizenship is required
Eligible to obtain and maintain a Government Security Clearance
Be at client site full time in Washington, DC
Bachelor’s degree or higher in data science, statistics, computer science, economics, mathematics, information systems, or similar field preferred
Minimum of three (3) years of professional experience (relevant advanced degrees may substitute equivalent number of years) with data science, AI/ML, or deep learning delivery responsibilities such as: Designing and developing data models and algorithms for multi-objective optimization and decision making, Building and deploying predictive and prescriptive analytics, Conducting hypothesis testing, statistical and probability analyses, Applying appropriate methods to describe statistical relationships between variables, Performing multiple correspondence analysis (MCA), principal component analysis (PCA, and association rule mining
Experience using tools such as Databricks; Azure’s Machine Learning and AI Services; AWS’s SageMaker and Bedrock
Experience programming in languages such as Python, R, Scala, SQL, JavaScript, C/C++, and Java
Experience using libraries and frameworks such as TensorFlow, PyTorch, mpi4py, Spark ML/MLlib, Jupyter, h2o, keras, or mir
Experience collecting in managing large data sets in cloud-based databases, data lakes, and data warehouses such as Oracle EDW, SQL Server, or MS Access
Ability to multi-task and stay flexible in a dynamic work environment

Nice To Haves

Active Government Security clearance
Experience developing algorithms for High-Performance Computing (HPC) environments using custom multi-core desktop computers and MS MPI
Experience with Deep Learning methods in NLP, sentiment analysis, topic modeling, and graph theory and databases
Experience working with big data distributed programming languages and ecosystems such as Hadoop, MapReduce, Pig, or Kafka
Experience working in an IT project team following SDLC and DevOps methodologies

Responsibilities

Conduct exploratory data analysis to evaluate data pipelines and construct data stores (structured, semi-structured, and unstructured) as needed to feed frameworks/models
Develop custom algorithms, frameworks, and models or leverage available tools, libraries, and applications to solve complex problems
Deploy data science and AI/ML solutions and develop methodologies to scale up
Present and articulate findings and present solutions to clients and team members
Collaborate with internal and external stakeholders to pattern analysis, predictive modeling, intelligent automation, GenAI, OCR, NLP, fraud detection, and other business cases for using data science, machine learning, and artificial intelligence
Maintain knowledge of advances of data science in industry and academia
Contribute to the overall growth of the Data and AI practice