Solution Architect

Job Profile

Principal Data Science Solution Architect and be a driving force in shaping the future of data-driven innovation. A strategic thinker with a passion for turning complex challenges into transformative solutions.

1. Data Science & Machine Learning:

• Proficiency in machine learning algorithms and techniques (supervised, unsupervised, reinforcement learning).
• Experience with data preprocessing, feature engineering, and model evaluation.
• Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch).

2. Programming & Software Development:

• Strong coding skills in languages such as Python, R, Java, or Scala.
• Experience with software development practices, including version control (Git), testing, and continuous integration/continuous deployment (CI/CD).

3. Big Data Technologies:

• Proficiency in big data frameworks and tools (e.g., Hadoop, Spark, Kafka).
• Experience with data warehousing solutions (e.g., Snowflake, Redshift) and distributed computing.

4. Data Management & Storage:

• Knowledge of SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB, Cassandra).
• Experience with data lakes and data warehouses.

5. Cloud Platforms:

• Proficiency in cloud services (e.g., AWS, Azure, Google Cloud) for deploying and managing data science solutions.
• Experience with cloud-based machine learning services (e.g., AWS SageMaker, Azure Machine Learning). Architectural Skills

6. System Architecture Design:

• Ability to design scalable, reliable, and secure architectures for data science solutions.
• Understanding of microservices architecture, RESTful APIs, and service-oriented architecture (SOA).

7. Integration & Interoperability:

• Experience in integrating various data sources and systems.
• Knowledge of API design and integration patterns.

8. Data Pipeline & Workflow Management:

• Proficiency in designing and managing ETL/ELT processes.
• Experience with workflow orchestration tools (e.g., Apache Airflow, Luigi). Analytical & Problem-Solving Skills

9. Analytical Thinking:

• Strong problem-solving skills and the ability to analyze complex datasets to derive actionable insights.
• Proficiency in statistical analysis and hypothesis testing.

10. Business Acumen:

• Ability to understand business problems and translate them into data science solutions.
• Experience in collaborating with stakeholders to gather requirements and deliver solutions that meet business needs. Communication & Collaboration Skills

11. Effective Communication:

• Strong verbal and written communication skills to explain complex technical concepts to non-technical stakeholders.
• Experience in creating technical documentation and presenting findings.

12. Collaboration & Leadership:

• Ability to lead cross-functional teams and collaborate with data scientists, engineers, and business analysts.
• Experience in mentoring and guiding junior team members.

Qualification

• Advanced degree (Ph.D. or Master's) in Computer Science, Data Science, or a related field.
• Proven experience (minimum of 10 years) in data science, with a focus on solution architecture and leadership.
• Expertise in machine learning, statistical modeling, data engineering, data modeling and data visualization.
• Strong programming skills in languages such as Python, R, or Scala.
• Experience with big data technologies and cloud platforms (e.g., AWS, Azure, GCP).
• Experience with AI/ML and cloud platforms (e.g., Data Bricks, Domino Data, Palantir AWS, Azure, GCP).
• Excellent leadership, communication, and stakeholder management skills.
• Demonstrated success in leading and delivering complex data science projects.
• Strong problem-solving and critical-thinking abilities

Strategic Leadership:

• Collaborate with executive leadership to define and execute the data science strategy aligned with business objectives.
• Provide thought leadership in data science and analytics, influencing decision-making at the highest levels. Drive innovation by identifying and implementing emerging technologies and methodologies.

Solution Architecture:

• Architect end-to-end data science solutions, ensuring scalability, reliability, and adherence to best practices. • Collaborate with cross-functional teams to define technical requirements and design robust data architectures.
• Lead the development and implementation of advanced analytics and machine learning models.

Team Management:

• Lead and mentor a team of data scientists and solution architects.
• Foster a collaborative and results-driven team culture, encouraging continuous learning and development.
• Provide guidance on complex technical challenges and drive excellence in project execution.

Client Collaboration:

• Collaborate with clients and internal stakeholders to understand business challenges and develop tailored data science solutions.
• Act as a trusted advisor, translating business needs into technical requirements and delivering high-impact solutions.
• Build and maintain strong client relationships, ensuring satisfaction and success.

Innovation and Research:

• Stay abreast of industry trends, emerging technologies, and best practices in data science.
• Lead research initiatives to explore and integrate new tools and methodologies.
• Drive a culture of innovation and continuous improvement within the data science team.

Quality Assurance:

• Establish and enforce data quality and governance standards.
• Conduct regular reviews and audits to ensure the integrity and accuracy of data-driven solutions.
• Collaborate with relevant stakeholders to address data-related challenges.