About the Role
A career in Products and Technology at PwC is an opportunity to bring PwC's strategy to life by driving products and technology into everything we deliver. The team collaborates with product strategy and product managers to govern readiness standards, ensuring compliance, privacy, and security by design for PwC’s technology assets. They provide guidance for product development across the lifecycle, from ideation to commercialization, and facilitate market readiness for technology assets as conditions change.
PwC Labs is focused on standardizing, automating, delivering tools and processes, and exploring emerging technologies that drive efficiency and enable people to reimagine the possible. Key areas of focus include process improvement, transformation, effective use of innovative technology and data & analytics, and leveraging alternative delivery solutions.
Day-to-Day Responsibilities
- Design and develop solutions related to machine learning, natural language processing, deep learning, and Generative AI to address business needs.
- Utilize the latest technologies creatively and analytically to apply cutting-edge techniques to specific challenges.
- Continuously expand personal skill sets and stay up to speed on the latest A.I. trends, tools, methodologies, and techniques.
Skills and Experience
Must Have
- Ideally 4 to 6 years of relevant experience.
- Bachelor’s Degree in Computer Science, Engineering, or other technical discipline (BE, BTech, MCA).
- Proficiency in development language environments such as Python, Java, Scala, R, SQL, and applying analytical methods to large and complex datasets leveraging these languages.
- Solid work exposure to Generative AI based projects, including designing and implementing solutions based on Langchain framework and designing efficient prompts for LLM’s.
- Good experience in pre-training and fine-tuning Large Language Models (LLM’s) on HuggingFace models and other Large Language Models.
- Prior experience on Azure cloud platform.
- Experience in machine learning, natural language processing, and deep learning.
- Proven ability with NLP and text-based extraction techniques.
- Familiarity with deep learning architectures used for text analysis, computer vision, and signal processing.
- Understanding of how to develop data science analytic models and operationalize them for automated contexts.
- Understanding of machine learning algorithms, such as k-NN, GBM, Neural Networks, Naive Bayes, SVM, and Decision Forests.
- Proficiency in commonly used data science packages including Spark, Pandas, SciPy, and NumPy.
- Experience manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources.
- Applying techniques such as multivariate regressions, Bayesian probabilities, clustering algorithms, machine learning, dynamic programming, stochastic-processes, queuing theory, and algorithmic knowledge to efficiently research and solve complex development problems.
- Developing and deploying A.I. solutions as part of a larger automation pipeline.
Good to Have
- Extensive abilities and/or a proven record of success in the application of statistical modeling, algorithms, data mining, and machine learning algorithms for problem-solving.
- A track record of delivery within a number of large-scale projects, demonstrating ownership of architecture solutions and managing change.
- Utilizing programming skills to write models directly usable in production as part of a large-scale system.
- Knowledge and application of technologies such as H20.ai, Google Machine Learning, and Deep learning.
- Developing end-to-end deep learning solutions for structured and unstructured data problems.
- Using common cloud computing platforms including Azure, AWS, and GCP for managing and manipulating large data sources, model development, and deployment.
- Visualizing and communicating analytical results using technologies such as HTML, JavaScript, D3, Tableau, and PowerBI.
Other Skills
- Documenting systems, refining requirements, self-identifying solutions, and communicating to the team.
- A desire to keep learning, maintain and expand one’s skill set across the full stack.
- A desire to improve the ‘status quo’, especially automating and improving software development and operations processes to achieve massively higher delivery velocity and operations quality.
- Contributing to thought leadership through participation in the development of technology processes.
- Applying continuous independent judgment while collaborating with others and influencing others within the project and domain teams.
- Building and leveraging relationships as well as specialist level verbal and written communication skills.
Preferred Certifications (at Least Two Certifications Are Preferred)
- Data Science Certifications in Machine Learning, Deep Learning, and Natural Language Processing.
- Certified Professional in Python Programming Level 1 or 2.