Senior Architect - Data Scientist

The Senior Architect - Data Scientist will be responsible for designing, developing, and implementing advanced data science solutions that address complex business challenges. This role requires a deep understanding of machine learning, statistical modeling, and data engineering, as well as the ability to architect scalable and efficient data systems. The ideal candidate will have a proven track record of leading data science projects, mentoring junior team members, and collaborating with cross-functional teams to deliver impactful results.

Key Responsibilities:

1. Solution Architecture:

○ Design and architect scalable, robust, and efficient data science solutions that meet business objectives.

○ Develop and implement advanced machine learning models and algorithms to solve complex problems.

○ Ensure the integration of data science solutions with existing systems and infrastructure.

1. Data Strategy & Innovation:

○ Define and drive the data science strategy, ensuring alignment with the companys overall goals and objectives.

○ Stay abreast of the latest advancements in data science, machine learning, and AI, and identify opportunities for innovation.

○ Lead the evaluation and adoption of new tools, technologies, and methodologies to enhance data science capabilities.

1. Project Leadership:

○ Lead end-to-end data science projects, from problem definition and data collection to model development, deployment, and monitoring.

○ Collaborate with cross-functional teams, including engineering, product management, and business stakeholders, to deliver data-driven solutions.

○ Manage project timelines, resources, and deliverables to ensure successful outcomes.

1. Mentorship & Team Development:

○ Mentor and guide junior data scientists, providing technical expertise and fostering a culture of continuous learning and development.

○ Conduct training sessions and workshops to enhance the team’s data science skills and knowledge.

○ Promote best practices in data science, including code quality, reproducibility, and ethical considerations.

1. Stakeholder Communication:

○ Communicate complex data science concepts and results to non-technical stakeholders in a clear and actionable manner.

○ Present findings, insights, and recommendations to senior leadership and influence decision-making processes.

○ Build strong relationships with stakeholders to understand their needs and ensure the delivery of value-added solutions.

Qualifications:

• Education:

○ Ph.D./Bachelor/ Master & degree in Computer Science, Statistics, Mathematics, or a related field with a strong focus on data science and machine learning.

• Experience:

○ 8+ years of experience in data science, machine learning, or a related field.

○ Proven experience in designing and implementing large-scale data science solutions.

○ Strong background in statistical modeling, machine learning, and data engineering.

○ Experience with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Hadoop, Spark).

○ Demonstrated leadership experience, including mentoring and managing teams.

• Technical Skills:

○ Proficiency in programming languages such as Python, R, or Scala.

○ Expertise in machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).

○ Strong knowledge of SQL and experience with relational and non-relational databases.

○ Familiarity with data visualization tools (e.g., Tableau, Power BI) and techniques.

○ Excellent knowledge of Generative AI (Transformer Models and Generative Adversarial Networks). At least one project in Transformer Models (Large Language Models) for Natural Language Processing. Or At-least 1 project in Transformer models or Generative Adversarial Network models for Image/Video generation or Speech Recognition.

○ Has worked on multiple Transformer Models (large Language Models) for Natural Language Processing. Has used Fine tuning and evaluating Large Language Models. Re- Infor cement Learning and Large Language Models powered applications. Prompt Engineering using Chat GPT.

• Soft Skills:

○ Excellent problem-solving and analytical skills.

○ Strong communication and interpersonal skills.

○ Ability to work collaboratively in a fast-paced, dynamic environment.

○ Strategic thinking and a results-oriented mindset.

Preferred Qualifications:

• Experience in [specific industry/sector] and familiarity with industry-specific challenges and opportunities.
• Published research or contributions to the data science community (e.g., papers, open-source projects).
• Certifications in data science, machine learning, or cloud platforms.