Data Science Engineer

JioTesseract, a digital arm of Reliance Industries, is India's leading and largest AR/VR organization with the mission to democratize mixed reality for India and the world. We make products at the cross of hardware, software, content and services with focus on making India the leader in spatial computing. We specialize in creating solutions in AR, VR and AI, with some of our notable products such as JioGlass, JioDive, 360 Streaming, Metaverse, AR/VR headsets for consumers and enterprise space.

About the Job :

We are looking for experience Data science Professionals across levels to implement and lead the development and execution of data science strategies that drive innovation and business impact.

Senior Levels of Data Science will oversee a team of data scientists, machine learning engineers, and data analysts, delivering high-quality data solutions across the organization. The role involves strategic leadership, hands-on technical expertise, and cross-functional collaboration to transform data into actionable insights and guide decision-making.

What Youll Be Doing :

Data Science Strategy and Vision :

- Define the overall data science strategy, aligning it with the organizations objectives and goals.

- Drive the adoption of advanced analytics, machine learning, and AI to solve complex business problems.

- Identify new opportunities to leverage data science for product innovation, customer insights, and operational efficiency.

Team Leadership and Development :

- Lead, mentor, and manage a growing team of data scientists, machine learning engineers, and data analysts.

- Foster a culture of innovation, collaboration, and continuous improvement within the data science team.

- Ensure professional development and skill growth by providing mentorship, setting clear goals, and conducting performance reviews.

Data Science Solutions Development :

- Oversee the development of predictive models, recommendation systems, and other advanced analytics solutions.

- Ensure the deployment of scalable and reliable machine learning models in production environments.

- Collaborate with product, engineering, and business teams to deliver data-driven solutions that create value.

Collaboration and Stakeholder Engagement :

- Partner with business leaders, product managers, and engineering teams to understand business requirements and define data science use cases.

- Act as a key point of contact for data science within the organization, advocating for data-driven decision-making.

- Communicate complex data science concepts to non-technical stakeholders, providing actionable insights and recommendations.

Data Governance and Quality :

- Establish best practices for data management, ensuring data quality, security, and compliance with relevant regulations.

- Work closely with data engineering teams to develop robust data pipelines, ensuring the availability of clean, reliable data for analysis.

- Implement ethical guidelines and governance frameworks to ensure responsible use of data and AI technologies.

Project and Resource Management :

- Oversee the planning, execution, and delivery of data science projects, ensuring that they are completed on time and within budget.

- Prioritize and manage multiple projects simultaneously, balancing short-term and long-term goals.

- Allocate resources effectively, ensuring that data science initiatives are aligned with the companys strategic priorities.

Research and Innovation :

- Stay updated on the latest trends, tools, and technologies in data science, machine learning, and AI.

- Lead research efforts to explore new data science methodologies, tools, and frameworks that can enhance business performance.

- Drive proof-of-concept (PoC) projects to validate new models, techniques, and approaches.

Measuring and Reporting Impact :

- Develop KPIs to measure the impact of data science initiatives on business outcomes.

- Present results and findings to executive leadership, showcasing the value created through data science.

- Continuously evaluate the effectiveness of data models and solutions, driving improvements and optimization where necessary.

What We Need To See :

- Experience in data science, machine learning, and advanced analytics, with a proven track record of delivering impactful data solutions.

- Minimum 5 years leadership experience in managing data science teams, including setting strategic direction and mentoring junior team members.

- Strong experience with statistical modeling, machine learning algorithms, and data visualization tools.

- Technical Expertise: R Python, Spark, Scala, SQL, Cloud (AWS / Azure / GCP), BERT, NLP, ChatGPT, Llama 2 & 3, Gemini 1.5 pro, ML frameworks - TensorFlow, PyTorch, CI/CD pipelines, Docker, Kubernetes, LangChain or Llama Index, Vector Database, CNN, RNN, LSTM

- Experience with big data technologies and platforms (e.g., Hadoop, Spark) and cloud environments (e.g., AWS, Google Cloud, Azure).

- Expertise in designing and implementing predictive models, natural language processing (NLP), and computer vision.

- Leadership Skills: Excellent leadership and people management skills, with the ability to inspire and grow a team.

- Strong communication and collaboration skills, with the ability to translate complex data science concepts into business terms.

- Proven ability to manage cross-functional teams and work in a matrixed environment.

- Business Acumen: Strong understanding of business strategy and the ability to align data science initiatives with key business goals.

- Proven ability to identify business opportunities through data and translate them into actionable data science solutions.

- Track record of influencing decision-making at the executive level through data-driven insights.