S&C Global Network - AI - Responsible AI - Sr Analyst

Entity:- Accenture Strategy & Consulting

Team:- Strategy & Consulting Global Network

Practice:- Responsible AI COE

Title:- Responsible AI Specialist/ Sr. Analyst

Job location:- Bangalore/Gurgaon/Pune/ Hyderabad/Chennai/Mumbai

The rapid development of AI is creating new opportunities to improve the lives of people around the world, from business to healthcare to education. As a result, it is also raising new questions about the best way to build fairness, interpretability, privacy and security into these systems.

The Data and AI revolution is changing everything. It's everywhere transforming how we work and play. Join Accenture and help transform leading companies and communities around the world.

We are looking for experienced and motivated individuals who will be a part of the

Responsible AI

Centre of Excellence (COE) within Accenture to join our multi-disciplinary Responsible AI team. We are committed to help people build AI products/solutions/services in a responsible and trustworthy manner.

The ideal candidate should have a strong client-facing consulting background in data science/analytics with an ability to pick up new technologies very quickly. He/she will be passionate about understanding the impact of AI systems on people and society and will have a track record in using tools to undertake assessments such as

Fairness/Bias, Explainability, Model Validation and Robustness, to assess model behaviour through a Responsibility lens.

Being a part of Accenture will help you grow both professionally and personally as you help shape our thinking and approaches to Responsible AI, working alongside world-class academics, industry leaders and practitioners. Responsible AI is a key strategic priority for Accenture and we're looking for the very best in the field to help us meet our ambitious goals in this space.

Responsibilities:

As a client-facing in Responsible AI, you will consult with Accenture's clients on how to design & develop reliable, effective user-centered AI systems in adherence to general best practices for software systems, together with practices that address responsibility considerations unique to AI & machine learning. You will also be expected to contribute to research on how AI systems can be designed holistically with fairness, interpretability, privacy, security, safety, and robustness built in by design.

As part of our team, you will:

Be a subject matter expert on technical aspects of Responsible AI & Data

Collaborate with colleagues to develop best practices, frameworks, tools for scalable implementation of Responsible AI in enterprises

Conduct research on Responsible AI policies, principles, issues, risk identification, risk remediation, regulatory requirements, latest trends etc.

Bring a strong conceptual understanding of Responsible AI, principles & tools with experience of using these tools in close collaboration with internal and external stakeholders/clients

Evaluate and implement technical best practices and tools for fairness, explainability, transparency, accountability, and other relevant aspects of Responsible AI

Develop a clear understanding of client's business issues to adopt the best approach to implement the Responsible AI Framework

Establish a consistent and collaborative presence by partnering with clients to understand the wider business goals, objectives & competitive constraints

Provide thought leadership by publishing in public forums/conferences/blogs on Responsible AI products, research or developments

Leading diverse and well-qualified RAI team.

Qualifications

Skillset Requirements:

Education:- PhD / Masters / Bachelors degree in Statistics / Economics / Mathematics /Computer Science / Physics or related disciplines from Premier Colleges in India or Abroad. Specialization in Data Science.

Must Have

3 10 years of Hands-on Data science experience in solving real life complex business problems

Minimum 1 years experience in enhancing AI systems to meet Responsible AI principles - Explainability, Fairness, Accountability, etc.

Passionate about understanding the impact of AI systems on people and society

Hands-on experience of using techniques such as data bias testing (e.g. for under-represented groups, proxy variables, recall bias, skew etc), Explainability (e.g. SHAP values, LIME), sensitivity testing, repeatability and similar to understand model limitations.

Demonstrated experience in writing reports that summarize analysis / assessments into simple and concise actionable points

Strong conceptual knowledge and practical experience in the Development, Validation, and Deployment of ML/AL models such as:

Supervised Learning - regression, classification techniques

Unsupervised Learning clustering techniques

Recommender Systems

Reinforcement Learning

Deep Learning Sequence models (RNN, GRU, LSTM etc.), CNN, GAN etc

Econometric models

Exploratory Data analysis, Hypothesis testing etc.

Comfortable in ingestion of technical whitepapers, legal policies, government regulations etc. in relation into Responsible AI and work with Academic partners to convert them into practice

Ability to learn and develop new methods, strategies and frameworks to proactively identify potential loopholes.

Comfortable with ambiguity, believe in first principles and have the skill to transform broad ideas into action plans

Excellent written and verbal communication skills with ability to clearly communicate ideas and results to both technical and non-technical business audience, such as senior leaders

Good time management skills to manage day-to-day work progress and ensure timely and high-quality deliverables

Self-motivated with ability to work independently across multiple projects and set priorities and Strong analytical bent of mind.

Good to have

Cloud Certifications (Azure / AWS / GCP)

Knowledge of AWS SageMaker Clarify / Azure Responsible ML and Fairlearn SDK / GCP AI Explanations

Experience in Chatbot Analytics, Web Crawling

Experience in MLOps tools like MLflow or Kubeflow

Knowledge of cybersecurity, vulnerability assessment, risk remediation etc.