Data Scientist for Financial Planning & Analysis (FP&A) at Leidos

Description

The Leidos Corporate Financial Planning & Analysis (FP&A) team is looking for an experienced Data Scientist with an entrepreneur’s mindset to support a new team working on projects to improve our Enterprise financial analytics and forecasting. The position will collaborate with the VP of FP&A in building a Data Analytics Team within Finance. You should be strongly experienced in Python coding and visualization building skills and have knowledge of commonly used ML libraries (SciKit-learn). Data Engineering and ML Ops experience is a plus!

We are looking for someone who is intellectually adaptive, likes to collaborate, inquisitive, and capable of conducting original science.

The FP&A Data & Analytics team will collaborate closely with Leidos’ AI Accelerator team which includes junior and senior research/data scientists and data engineers with expertise in information retrieval, UI development, information science, machine learning and artificial intelligence, and statistics.

The Data Scientist will be expected to build statistical models, test hypotheses, interpret, summarize, visualize, and succinctly report on data findings. The Scientist will leverage automation and machine learning to manage data, predict scenarios and make recommendations. The data scientist will partner with business and operational leaders to provide an impact by leveraging data and analytical tools, strategic thinking, and hypothesis-based analysis on machine learning (ML)-based projects. The data scientist is responsible for modeling complex business problems through statistical, algorithmic, mining, and visualization techniques. Further, they will support senior leadership by creating business insights, reports, and analyses to aid in the decision-making process.

Responsibilities:

• Translates business needs into analytics/reporting requirements to support executive decisions and workflows with required information
• Performs large-scale experimentation and builds data-driven models to answer business questions
• Proactively mines data warehouses to identify trends and patterns and generates insights for business units and senior leadership
• Performs large-scale experimentation to identify hidden relationships between variables in large datasets
• Researches and implements cutting-edge techniques and tools in machine learning/deep learning/artificial intelligence to make data analysis more efficient
• Designs and conducts data analyses with the highest standard of rigor and scientific accuracy; this includes study design, methodology, algorithms, and statistical modeling
• Analyzes data provided to identify trends, inform decisions
• Supports developmental plans based on data findings related to program, personnel, training needs
• Supports development and maintenance of primary program database and run interval reporting
• Implements appropriate modeling and data science strategies required to address customer needs
• Communicates results and methods for solutions to internal and external stakeholders
• Designs, builds, trains, and evaluates machine learning models

Basic Qualifications:

• Bachelor's degree in Computer Science, Data Science, Data Engineering or related field with 2+ years of relevant experience
• Strong experience with Python as well as fluency in multiple programming languages and statistical analysis tools such as C++, JavaScript, R, SAS, Excel, SQL, MATLAB, SPSS
• Experience /familiarity with frequentist statistics and probability including predicative modeling
• Experience with data repositories and reporting tools
• Good understanding of machine learning algorithms, tools and platforms
• Experience with AI/ML tools, such as common Python packages (e.g., scikit-learn, NumPy, Pandas) and Jupyter notebooks
• Experience with time series modeling, causal inference, or probabilistic forecasting models.
• Experience with tabular data analysis using languages such as SQL, R, and/or Python
• Experience with statistical modeling and data analysis
• Understanding of transformers and foundation models
• Self-starter with high intellectual curiosity
• Great communication skills, able to explain model results to a non-technical audience
• Proficient in data exploration techniques and tools
• Ability to work in a cross functional team as this position will be under the Finance function but have opportunity to collaborate with the AI Accelerator team
• US citizenship is required and able to obtain security clearance as needed.

Preferred Qualifications:

• Experience with data visualization libraries such as Plotly, Streamlit, and matplotlib
• Willing to learn new skills and platforms to support data analytics
• Candidates will ideally have a specialization in ML or AI
• Experience with database technologies such as SQL, NoSQL, Oracle, Hadoop, or Teradata
• Proficient in data exploration techniques and tools such as Amazon Web Services (AWS)
• Experience with MLOps tools and frameworks, such as Kubeflow, MLflow, DVC, TensorBoard
• Ability to design, build, and deploy data solutions that capture, explore, transform, and utilize data to support AI, ML, and BI
• Experience with data repositories and ETL
• Knowledge of how to design and implement high-volume data ingestion and streaming pipelines using Open Source frameworks like Apache Spark, Flink, Nifi, and Kafka on AWS Cloud
• Ability to integrate data from different sources, including databases, data warehouses, APIs, and external system

Original Posting Date:

While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

Pay Range:

The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.