Role Overview

As a data scientist in the Labor Market Intelligence (LMI) team in HC Forward, you will work with billions of job postings and employee profile records to extract labor market insights, create products, and enable services for Fortune 500 clients. The ideal candidate will make use of Deloitte s extensive labor market data combined with government labor and population data to inform and drive clients hiring and overall growth and investment strategies. As such, deep knowledge of labor market data and its standard analytical approaches, as well as confidence working with very large datasets and unstructured data, are integral for success in this position.

Required Skills Core Technical Competencies

- Advanced Python Programming: Expertise in Python with production-level code quality, including OOP, API development, and best practices (linting, testing, documentation), converting notebook driven Data Science into pipeline ready scripts.
- Machine Learning Expertise: Deep understanding and practical application of:
- Classical Supervised and Unsupervised ML algorithms (Regression/Classification, Random Forests, Gradient Boosting, SVM, Clustering)
- Deep Learning frameworks (TensorFlow, Keras, PyTorch)
- Analytical thinking, problem-solving, and statistical methods (causal inference, A/B testing, experimental design, etc.)
- Time series forecasting and anomaly detection
- Model evaluation, validation, and optimization techniques

- NLP Expertise:

- Classical NLP techniques/models (text preprocessing, feature extraction, representation learning, vectorization/embeddings, TFIDF, NB classifiers, NMF, topic modeling, dimensionality reduction)
- Experience with up-to-date NLP techniques including transformer models (BERT, GPT)




- Practical applications: sentiment analysis, document classification, named entity recognition
- Working knowledge of NLP libraries (NLTK, spaCy, Hugging Face Transformers)

- Data Engineering:

- Expertise in SQL (PostgreSQL/Redshift preferred)
- Experience in partnering with Data Engineering teams, co-developing data pipelines, ETL processes, and handling large-scale datasets (GB+ scale)

- Cloud Platforms: Hands-on experience with at least one major cloud platform (AWS, Azure, GCP), including:

- Managed ML services (any of SageMaker, Azure ML, Vertex AI)
- Containerization and orchestration (Docker, optionally Kubernetes)
- Serverless architectures for ML deployment (optional)

Technical Stack
- Standard Python DS/ML libraries: Numpy, Pandas, Tensorflow, Keras, Matplotlib, Seaborn, Scikit-learn, Statsmodels, SciPy, Sktime, Prophet, NLTK, Spacy
- Big Data/Distributed Compute: PySpark, Dask, or similar distributed computing frameworks
- Dashboard/Prototyping: Streamlit, Plotly, Flask
- Version Control & CI/CD: Git workflows and deployment pipelines
- Database Systems: SQL proficiency, vector databases

Specialized Domain Experience
- Experience in HR Analytics, People Analytics, or Workforce Planning
- Experience with public government labor and census data from both US (BLS, Census) and, preferably, international sources
- Experience with and knowledge of standardized occupational/skills databases and conventions (SOC, ONET, ESCO, NOC, ANZSCO, ISCO-08)

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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