Analytics & AI Site Leader

Accountabilities:

No. Key Accountabilities:

1. Define the long-term strategic vision for Analytics and AI products, aligned with the organization's goals. Identify opportunities for leveraging Analytics and AI to drive business growth and innovation.

2. Create and maintain a product roadmap that outlines the priorities and timeline for Analytics and AI initiatives. Prioritize features and enhancements based on business value, customer feedback, and market trends.

3. Collaborate with various stakeholders, including business leaders, data scientists, engineers, and designers, to gather input and feedback. Act as the liaison between business units and technical teams to ensure clear communication and alignment.

4. Elicit and document detailed requirements for Analytics and AI products, considering both functional and non- functional aspects. Translate business needs into actionable tasks for the development team.

5. Work closely with development teams (data engineering, machine learning, etc.) to ensure the successful execution of projects. Monitor project progress, address issues, and make adjustments to the roadmap as necessary.

6. Define acceptance criteria and conduct user acceptance testing (UAT) to ensure product quality.

- Ensure that data privacy and security standards are adhered to throughout the product development lifecycle.

- Ensure compliance with data regulations (e.g., DPDP, IT ACT 2000) and best practices for data governance.

7. Collect and analyze user feedback and usage data to identify opportunities for product improvement. Iterate on product features and functionality to enhance user experience.

8. Plan and execute product launches, including marketing and training initiatives. Drive user adoption and measure the impact of data and AI products on business KPIs.

9. Maintain comprehensive product documentation, including user guides and release notes. Provide regular updates to stakeholders on the status of product development and key performance metrics.

Experience:

- Strong, proven programming skills in Python, C/C++, Java, R , MATLAB, Scala, and with machine learning and deep learning and Big data frameworks including TensorFlow, Caffe, Spark, Hadoop, Kafka, Hive, Oozie, Map Reduce, Cassandra etc. Hands on experience in AWS technologies like AWS S3, RedShift, Glue, Lambda, EMR & Athena. Experience with writing complex programs and implementing custom algorithms in these and other environments.

- Strong Knowledge of emerging technologies such as AI, Machine Learning, Deep Learning, Data Science, NLP,Computer Vision, Internet of Things (IoT) and hands-on experience with structured/unstructured data/text/image based descriptive/predictive analytics.

- Familiarity with Agile tools and development methodologies (e.g., JIRA, Trello).

- Experience in data management, data analytics middleware, open-source frameworks and workbenches, platforms and infrastructure, Google and Azure based cloud technologies. Experience with fogging is a plus.

- Excellent problem-solving, conflict resolution and critical-thinking skills. Strong communication and presentation skills.

- Understand the financial aspects of product management, including budgeting and cost analysis.

- Knowledge of DevOps practices for data pipelines.

- Strong ability to work effectively and independently in a fast-paced global collaborative agile team environment with tight deadlines

- A flexible, pragmatic and collaborative team player with innate ability to engage with stakeholders at all levels in the organization.

- A self-starter with high levels of drive, energy, resilience and a strong desire for professional excellence with a passion for data and data science

- Awareness of the ethical implications of Analytics and AI, and the ability to make responsible decisions regarding data usage

- A solid understanding of the organization's industry, market, and competitive landscape to make informed product decisions.

- A customer-focused mindset with the ability to gather and analyze user feedback for Analytics and AI to drive product improvements and enhancements.

- Skilled in identifying potential risks and issues related to Analytics and AI projects and developing mitigation strategies.

Educational Experience:

- MS or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or related technical field.

- About 10-12 years' experience in applying AI/NLP, Machine Learning/Deep Learning, data-driven statistical analysis & modelling solutions. Last 5 years' experience in leading and mentoring teams.

- Experience in Metals, Mining and Commodity trading is a definite plus

Age : Mid 30's to early 40's