Corporate AI and Machine Learning Engineer at Booz Allen… · McLean

Summary: Booz Allen Hamilton is a leader in consulting and technology solutions, seeking a Corporate AI and Machine Learning Engineer to leverage data for problem-solving. The role involves developing analytical processes and tools, conducting statistical analyses, and supporting talent analytics efforts to drive decision-making across the organization. Responsibilities: - Leverage advanced analytical expertise to inform data storytelling, providing recommendations that easily translate into the language of the business for action - Support company-wide talent analytics efforts across key areas such as talent acquisition, compensation, and attrition to drive decision-making - Conduct statistical analysis to identify differences and trends, and develop predictive models - Work with People Services and business leaders on their team's analytics needs, propose solutions, and develop people analytics strategies that align with company and business objectives across functional areas - Communicate effectively across a highly matrixed organization and at different levels, think and act strategically, and influence key leaders - Integrate with other internal analytics teams to partner on data sets which integrate the talent story with other functional analytics, including our internal enterprise analytics office, as needed - Support ad-hoc analytical requests using quantitative and qualitative people data to provide insights across the employee lifecycle to guide leader decision-making processes aligned with their priorities Required Qualifications: - 1+ years of experience using R or Python for data manipulation, statistical analysis, and machine learning model development - Experience utilizing SQL for querying and manipulating data - Experience with statistical inference, hypothesis testing, experimental design, and causal analysis - Experience applying experimental design and A/B testing, causal inference, time series or forecasting, survival or attrition modeling, multilevel or hierarchical models, or survey analytics - Experience with natural language processing techniques and large language models (LLMs) - Knowledge of compliance and regulatory requirements associated with data management and how to handle data with strict confidentiality - Ability to pick up new tools and concepts quickly such as building depth in Spark, ML engineering, and enterprise-scale data practices - Ability to maintain a high level of competency in analytical principles, tools, and techniques to discover, learn, and apply new best-in-class advances in talent data analytics capabilities - Ability to translate technical results into clear insights and business recommendations for senior-level audiences, document work, and communicate findings to technical and non-technical stakeholders - Bachelor's degree in Statistics, CS, Industrial Psychology, Organizational Psychology, Applied Mathematics, or Data Science Preferred Qualifications: - Experience with supervised and unsupervised machine learning algorithms relevant to predictive modeling, classification, regression, and clustering - Experience working with HRIS or ATS, survey, or people-related datasets, including Workday, and employee lifecycle processes such as talent acquisition, talent development, workforce planning, or employee listening - Experience utilizing PySpark or Scala for large-scale data processing and Azure Databricks platform and notebooks - Experience using MLflow for tracking or registry, model versioning, and experiment management in cloud environments, including Databricks - Experience using PyTorch and transformer-based models - Experience leading functional projects as a people analytics subject matter expert - Knowledge of the government contracting business and people data analytics, people processes, and people metrics, including employee experience survey design and overall workforce lifecycle listening strategies - Knowledge of relational modeling concepts such as fact vs. dimension tables, primary or foreign keys, constraints, or slowly changing dimensions - Ability to follow emerging team CI/CD practices in GitHub - Master's degree in a quantitative field such as Statistics, Applied Machine Learning, Analytics, Quant Psychology, Economics, or Biostatistics Required Skills: Python, R, SQL, Statistical analysis, Machine learning, Experimental design, A/B testing, Causal inference, Time series analysis, Natural language processing, Large language models, Data storytelling, Spark, ML engineering, Talent analytics, Data confidentiality compliance, Influencing stakeholders, Strategic thinking Benefits: Health, Life, Disability, Financial, Retirement benefits, Paid leave, Professional development, Tuition assistance, Work-life programs, Dependent care