Data Science Architect

**About US:

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We turn customer challenges into growth opportunities.

Material is a global strategy partner to the world’s most recognizable brands and innovative companies. Our people around the globe thrive by helping organizations design and deliver rewarding customer experiences.

We use deep human insights, design innovation and data to create experiences powered by modern technology. Our approaches speed engagement and growth for the companies we work with and transform relationships between businesses and the people they serve.

Srijan, a Material company, is a renowned global digital engineering firm with a reputation for solving complex technology problems using their deep technology expertise and leveraging strategic partnerships with top-tier technology partners. Be a part of an Awesome Tribe

**Job Summary**:
**Position Overview**:
As the Data Science Architect, you will play a crucial role in technical leadership, overseeing the entire data science lifecycle, and collaborating with cross functional teams. This position requires hands-on experience in architecting and implementing scalable data science solutions, along with a deep understanding of machine learning, deep learning techniques and statistical modeling. Leadership and mentorship skills are essential for guiding a team of data scientists, machine learning engineers, and backend engineers. You will be instrumental in mentoring, shaping and executing the data science strategy, ensuring the successful deployment and orchestration of models in collaboration with Operations teams.

**Responsibilities**:

- Demonstrated expertise with 8-12 years of hands-on experience in AI/ML, NLP, Python, and cloud platforms such as AWS/Azure/GCP, Docker, and Kubernetes.
- Provide architectural vision for data science projects, ensuring alignment with overall business objectives.
- Architect and design scalable solutions for various AI/ML solutions, tailored to specific requirements, spanning NLP, Computer/Machine Vision, Speech Recognition, Clustering, Segmentation, and Deep Learning.
- Spearhead the conversation with technical and operation teams to facilitate the deployment and orchestration of models, ensuring seamless integration into production systems.
- Provide architectural guidance to Ops teams to enhance the scalability and efficiency of deployed models.
- Offer technical leadership and mentorship to the data science, machine learning, and backend engineering teams, emphasizing architectural best practices.
- Foster a collaborative and innovative team culture, promoting continuous learning and development in architectural design and implementation.
- Stay abreast of industry trends and emerging technologies in data science and architecture, bringing innovative ideas to enhance the organization's technical capabilities.
- Effectively communicate complex technical concepts to both technical and non-technical stakeholders, ensuring a shared understanding of architectural decisions and implications.
- Collaborate with key stakeholders, including business leaders and project managers, to align technical strategies with business goals.
- Implement robust quality assurance processes to validate the accuracy and reliability of implemented models.
- Collaborate with data quality teams to ensure high-quality, consistent, and reliable results.
- Ensure thorough documentation of architectural designs, model implementations, and deployment processes, fostering transparency and knowledge sharing within the team.
- Drive continuous improvement initiatives in data science architecture, incorporating feedback and lessons learned from model deployments to enhance future projects.
- Establish and enforce best software engineering practices, accommodating common design patterns and Object-Oriented Programming (OOPs) principles, coding standards, documentation practices, and model version control within the team.

**Must Have Skills**:

- 8+ years of relevant data science experience in a delivery focused team
- Strong coding ability in Python, familiarity with relevant libraries (e.g., TensorFlow, PyTorch), and general software development skills (source code management, debugging, testing, deployment, etc.) is a must
- Advanced proficiency in Python and relevant libraries/frameworks for data science and machine learning.
- Demonstrated hands-on experience in architecting and implementing scalable data science solutions leveraging cloud services like AWS / Azure / GCP
- Strong architectural design skills building highly scalable microservices and event driven solutions leveraging Cloud, Docker and Kubernetes, with the ability to provide a vision for data science projects aligned with overall business objectives.
- Leadership experience guiding cross-functional teams in the development and deployment of AI solutions.
- Proven experience overseeing the complete data science and modeling lifecycle, from ideation to deployment, ensuring projects are delivered on