WHAT YOU'LL DO

• This role is part of the Globle People Analytics team with a dedicated focus to support and drive the recruiting analytics agenda for BCG.
• As a Global People Analytics Senior Analyst, you’ll support evidenced-based decision making. You’ll provide dats science & analytical inputs to support the global HR recruiting teams and work with stakeholders across BCG. You’ll add a data science & quantitative perspective to discussions on new and existing processes and policies. You will apply Data Science and analytics mindset and empower recruiters and the recruiting leadership teams with Statistical Inferences through Tableau dashboards, and presentations to help improve processes and solve recruiting related challenges.

YOU'RE GOOD AT

• Developing and implementing advanced statistical models, pipelines, and machine learning algorithms to solve business problems and optimize processes.
• Designing, developing, and maintaining Tableau dashboards and reports.
• Creating data models, data visualizations, and interactive dashboards using Tableau
• Troubleshooting and resolving issues related to Tableau dashboards and data sources.
• Designing and building predictive models, classifiers, and recommendation systems to enhance business performance.
• Extracting and transforming data from various sources, ensuring data quality and integrity throughout the process.
• Collaborating with cross-functional teams to identify data requirements and define project objectives.
• Staying updated with the latest advancements in tableau, alteryx, data science, machine learning, and AI technologies, and apply them to improve existing methodologies and processes.
• Playing a central role in all stages of the AI product development life cycle, including:
• Designing Machine Learning systems and model scaling strategies & Run necessary ML tests and benchmarks for model validation
• Fine-tune, retrain and scale existing model deployments
• Generating insights from large and complex datasets, and understanding the nuances and inconsistencies in data
• Ability to multi-task and operate effectively in a fast-paced and customer-oriented environment; ability to manage multiple stakeholders in a matrix organization
• Communicating and presenting technical details to non-technical stakeholders
• Strong interpersonal skills, who showcases credibility and excels in a collaborative setting
   

YOU BRING (EXPERIENCE & QUALIFICATIONS)

• Undergraduate degree, preferably in an engineering or other technology-related fields, with high academic achievement required; advanced degree preferred 
• 3-5/ 5+ years of full-time experience in experience in data science, statisticalodeling, and machine learning & NLP
• Strong proficiency in Tableau Desktop,Tableau Server
• Experience in Strong proficiency in Tableau Desktop and Tableau Server.
• Experience in data visualization, data modeling, and dashboard design principles.
• Expertise in Python, Natural Language Processing, Data science & Machine Learning, Statistics, Linear Algebra, Excel
• Hands-on experience of data manipulation tools such as Alteryx, SQL, Tableau Prep. Etc.
• Deep interest and aptitude in data, metrics and analysis
• Exposure to some of the following technologies is needed – Flask, Azure Data Bricks, GitHub, Snowflake would be added an advantage
   

YOU'LL WORK WITH

• You will work with multiple stakeholders.
• Work closely with the global recruitment leadership to understand pain points and opportunity areas
• Work with global recruiting and analytics teams to find synergies and expand reporting capabilities across BCG recruitment teams
• Collaborate with colleagues across other HR and analytics teams to enhance the current reporting infrastructure for recruiting
   

ADDITIONAL INFORMATION

• Requirements
  • Strong programming skills in languages such as Python or R, with experience in data manipulation, analysis, and modeling libraries (e.g., Pandas, NumPy, scikit-learn).
  • Strong proficiency in Tableau Desktop and Tableau Server.
  • Experience in data visualization, data modeling, and dashboard design principles.
  • Solid exposure to BERT , Large Language Model & other NLP models
  • Solid understanding of statistical concepts and methodologies, including regression, classification, clustering, time series analysis, and experimental design.
  • Experience with data visualization tools such as Tableau, Power BI, or matplotlib.
  • Proficient in using SQL and working with relational databases.
  • Excellent knowledge of ML & Deep Learning domain
  • Open to learning new technologies and programming languages as required
  • Experience collaborating with and understanding the needs of stakeholders from a variety of business functions: Product, Engineering and Technical Operations
  • Excellent communication skills, both written and verbal, with the ability to effectively convey technical concepts to non-technical stakeholders.
  • Strong attention to detail, ability to handle and analyze large datasets, and a commitment to delivering high-quality results within deadlines.
• Responsibilities
  • Develop and implement advanced statistical models, pipelines, and machine learning algorithms to solve business problems and optimize processes.
  • Develop and maintain dashboards using Tableau and Alteryx/Tableau Prep/SQL
  • Collaborate with cross-functional teams to identify data requirements and define project objectives.
  • Design and build predictive models, classifiers, and recommendation systems to enhance business performance.
  • Extract and transform data from various sources, ensuring data quality and integrity throughout the process.
  • Communicate complex findings and technical concepts to non-technical stakeholders through visualizations, presentations, and reports.
  • Stay updated with the latest advancements in data science, machine learning, and AI technologies, and apply them to improve existing methodologies and processes.
  • Participate in the development of data science best practices, standards, and guidelines within the organization.
  • Play a central role in all stages of the AI product development life cycle, including:
  • Designing Machine Learning systems and model scaling strategies
  • Research & Implement ML and Deep learning algorithms for production
  • Run necessary ML tests and benchmarks for model validation
  • Fine-tune, retrain and scale existing model deployments
  • Extend existing ML library’s and write packages for reproducing components
  • Partner with business leaders, domain experts, and end-users to gain business understanding, data understanding, and collect requirements
  • Interpret results and present them to business leaders
  • Perform code reviews & optimization for your projects and team
  • Lead and mentor by example, including projects