ETIC, Senior Data Scientist

Line of Service

Advisory

Industry/Sector

Technology

Specialism

Advisory - Other

Management Level

Senior Associate

Job Description & Summary

Data Science Engineer leverages machine learning, data science, and AI technologies to tackle complex business problems and provide data-driven insights across diverse industries. This role involves developing predictive models, conducting data analysis, and optimizing business processes through intelligent systems in areas such as classification, forecasting, customer segmentation, anomaly detection, and generative AI applications like Retrieval-Augmented Generation (RAG).

Key Responsibilities: 

• Design, develop, and deploy machine learning models and data-driven solutions for various applications, such as predictive analytics, business forecasting, customer insights, and generative AI applications. 

• Build and maintain end-to-end data science and machine learning pipelines for scalable model development, training, and deployment in production environments. 

• Conduct exploratory data analysis (EDA) and feature engineering to derive actionable insights from structured and unstructured data. 

• Develop and implement retrieval-augmented generation (RAG) models, combining search-based retrieval with generative models to enhance the performance of NLP tasks like document generation, question answering, and knowledge retrieval. 

• Collaborate with stakeholders to understand business challenges and translate them into data science and machine learning problems. 

• Continuously evaluate and refine models and algorithms, ensuring accuracy, robustness, and relevance as business needs evolve. 

• Stay up-to-date with the latest trends and advancements in machine learning, AI, data science, and generative models to integrate cutting-edge techniques into solutions. 

Required Skills & Experience: 

• 4+ years of experience in AI/ML engineering, data science, with a strong focus on building, deploying, and scaling data-driven solutions. 

• Experience in data science fundamentals such as statistical analysis, hypothesis testing, data cleaning, and visualization. 

• Proficiency in Python and essential libraries for data science, including pandas, NumPy, Matplotlib, seaborn, and scikit-learn for model building and data analysis. 

• Strong experience with machine learning algorithms and techniques, including supervised and unsupervised learning (e.g., regression, classification, clustering, PCA). 

• Hands-on experience with deep learning techniques and frameworks such as TensorFlow, PyTorch, and Keras for neural networks. 

• Familiarity with Natural Language Processing (NLP) using tools such as spaCy, NLTK, Hugging Face Transformers, and knowledge of models like BERT, GPT, and T5 for text analysis, sentiment analysis, and language modeling. 

• Experience with retrieval-augmented generation (RAG) models, including combining information retrieval and generative models for tasks like document generation, question answering, and knowledge-based dialogue systems. 

• Familiarity with generative AI technologies, including GANs (Generative Adversarial Networks), VAE (Variational Autoencoders), and transformer-based models for various generative tasks such as content generation and image synthesis. 

• Experience with model evaluation techniques like cross-validation, A/B testing, and hyperparameter tuning to ensure model robustness and reliability. 

• Knowledge of cloud platforms for deploying models at scale, such as AWS SageMaker, Google Cloud AI, or Azure Machine Learning. 

• Strong ability to perform exploratory data analysis (EDA), identifying trends, correlations, and outliers to drive actionable insights. 

• Experience in data visualization to communicate findings effectively using tools like Matplotlib, Seaborn, Tableau, or Power BI. 

• Solid understanding of data preprocessing, including feature engineering, normalization, and handling missing data to prepare datasets for analysis and model training. 

• Strong analytical thinking and the ability to work with large, complex datasets to derive actionable insights and support business decisions. 

 

 

Education (if blank, degree and/or field of study not specified)

Degrees/Field of Study required:

Degrees/Field of Study preferred:

Certifications (if blank, certifications not specified)

Required Skills

Optional Skills

Accepting Feedback, Accepting Feedback, Active Listening, Agile Scalability, Amazon Web Services (AWS), Analytical Thinking, Apache Hadoop, Azure Data Factory, Communication, Creativity, Data Anonymization, Database Administration, Database Management System (DBMS), Database Optimization, Database Security Best Practices, Data Engineering, Data Engineering Platforms, Data Infrastructure, Data Integration, Data Lake, Data Modeling, Data Pipeline, Data Quality, Data Transformation, Data Validation {+ 18 more}

Desired Languages (If blank, desired languages not specified)

Travel Requirements

0%

Available for Work Visa Sponsorship?

No

Government Clearance Required?

No

Job Posting End Date