We are HR Data and Analytics, a centralized global team responsible for all aspects of workforce data strategy, analytics and reporting, data governance, and artificial intelligence and machine learning (AI/ML) based solutions. We are looking for strong industry experience in quantitative and statistical modeling, data mining, insights delivery, and innovative mindset to analyze large-scale multi-dimensional workforce data. You will be a key contributor and collaborator to our intellectual capital by developing intelligent discovery and decisioning tools.
As a Data Scientist – Senior Associate within the Analytics Team, you will actively engage in analyzing large-scale data to develop analytical, statistical, and data science models that reveal underlying patterns and address business questions related to employee relations, conduct, customer complaints, and talent insights. In this team member contributor role, you will collaborate with various internal stakeholders, translate business inquiries into analytical frameworks, work alongside subject matter experts, and master workforce data. You will implement solutions such as analytics dashboards, proprietary models, and visualization schemes, while continuously embracing learning and innovation by adopting the latest tools and technologies.
Job responsibilities:
- Build and deliver statistical explanatory models, repeatable-scalable analytics workflows, leverage AI tools – from quality checks, feature engineering, to model performance evaluation, and collaborate with technology teams in seamless deployment and operational enhancement in support of HR and partner business functions’ evidence-based data-driven decision.
- Build custom visualization and reporting solutions to communicate insights, trends, and recurring solutions needed for end-user consumption and cross-functional teams in business & technology
- Customize commercial or open-sources analytical solutions, create new algorithms to build proprietary solutions contributing to intellectual capital of the organization.
- Embrace attention to detail, accountability, rigor, timeliness, and robustness in presenting results to a broad spectrum of stakeholders via reports, PowerPoint decks, and insightful visualizations.
- Capture and understand business processes, end-user requirements, translate into customized analytical solutions, communicate results.
- Create and document institutional knowledge from workforce data insights, models, and share such knowledge with relevant team members and stakeholders
- Adhere to various control functions directives, documentation, data protection policies, and regulatory requirements while handling proprietary and sensitive data.
Required qualifications, capabilities, and skills:
- Bachelor’s degree with 5+ years of experience or a Master’s degree with 3+ years in a relevant quantitative field.
- Proficiency in quantitative and statistical data modeling tools (e.g., Python, R, scikit-learn etc.) to implement a variety of methods (e.g., hypotheses testing, multiple regression, multivariate analyses), exploratory (e.g., clustering, multi-dimensional scaling), anomaly detection, and AI-ML techniques (e.g., supervised / unsupervised / reinforcement learning).
- Proficiency in data wrangling, transformation, end-to-end workflows for complex multi-dimensional data, and automation (e.g., SQL, Alteryx, Business Objects, DataBricks, etc.)
- Expertise in data analytics and visualization tools such as Tableau or Power BI.
- Expertise in one or more cloud and supporting data analytics frameworks, such as various AWS data processing services, SageMaker, Starburst, Databricks, SnowFlake, etc.
- Strong understanding of mathematical concepts and application of statistical pattern recognition (e.g., PCA, correlations), algorithms (e.g., logistic regression, gradient boosting, support vector machines, K-means), model interpretation, cost functions, and performance evaluation (e.g., ROC, hyper parameter tuning)
- Experience in text mining and NLP analytics, such as customer/employee survey analyses, unstructured data, segment analysis, pattern detection from topic modeling, etc., using variety of commercial or open source techniques.
- Experience with data analytics tools and frameworks, such as AI tools, feature engineering, and model performance evaluation.
- Experience with data platforms like Databricks, SnowFlake, or similar technologies.
- Strong client engagement and technical project execution skills and demonstrated industry experience as individual contributor within larger teams collaborating across multiple concurrent priorities.
Preferred qualifications, capabilities, and skills:
- Experience with graph databases and network analyses.
- Familiarity with Generative AI techniques.
- Experience in client engagement or technical project execution.
- Familiarity domain knowledge or prior experience in HR, people analytics employee relations, recruitment, compensation, labor market research, customer interaction data preferably in the financial services industry.
- Experience managing ambiguity, articulating complex issues in easy-to-understand ways
- Experience with project management concepts, such as JIRA, BitBucket, etc.
