About the Role
A career in Products and Technology offers the opportunity to contribute to PwC's strategy by integrating products and technology into our service delivery. Our clients expect us to leverage the right people and technology to address their challenges, and Products and Technology aims to help PwC meet this demand and accelerate business growth. We comprise skilled technologists, data scientists, product managers, and business strategists who utilize technology to drive change.
Our team collaborates with product strategy and product managers to ensure readiness standards, adhering to principles such as compliance, privacy, and security by design for PwC’s technology assets to succeed in the market. We provide guidance for product development throughout its lifecycle, from ideation and strategy to commercialization and monetization. Additionally, we facilitate market readiness for technology assets as changes occur to assets or market conditions.
PwC Labs is dedicated to standardizing, automating, delivering tools and processes, and exploring emerging technologies that enhance efficiency and enable our people to reimagine possibilities. Key areas of focus include process improvement, transformation, effective use of innovative technology, data & analytics, and leveraging alternative delivery solutions to generate additional value for our firm. If you are a professional seeking to apply your skills in a product-based, fast-paced, entrepreneurial, and inclusive environment, PwC Labs is the team for you.
A career in PwC Labs provides a unique opportunity to build transformative products and innovate mechanisms that offer new insights to our business and customers, helping to identify business gaps, solve problems, and create new business opportunities.
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 to work creatively and analytically, applying cutting-edge techniques to specific challenges.
- Continuously expand personal skill sets and stay updated on the latest AI 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 using these languages.
- Solid work exposure to Generative AI based projects, including designing and implementing solutions based on the Langchain framework and designing efficient prompts for LLMs.
- Good experience in pre-training and fine-tuning Large Language Models (LLMs) on HuggingFace models and other LLMs.
- Prior experience on the 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 not only how to develop data science analytic models but also how to operationalize these models 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.
- Ability to lead, train, and work with other data scientists in designing effective analytical approaches, considering performance and scalability for large datasets.
- 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 and apply engineering methods to define, predict, and evaluate results.
- Developing and deploying AI 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.
- Proficiency in programming skills and knowledge of how to write models that can be directly used in production as part of a large-scale system.
- Proficiency in technologies such as H20.ai, Google Machine Learning, and Deep learning.
- Experience developing end-to-end deep learning solutions for structured and unstructured data problems.
- Experience using common cloud computing platforms, including Azure, AWS, and GCP, in addition to their respective utilities for managing and manipulating large data sources, model development, and deployment.
- Proficiency in 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 for continuous learning, maintaining one’s skill set, staying up to date, and expanding knowledge 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.