Data Science Solution Architect

Job description

As a Data Science Solution Architect at Tredence, you will lead the design and development of data science solutions that leverage advanced analytics, machine learning, and artificial intelligence to address complex business challenges. You will work closely with cross-functional teams to understand business requirements and translate them into scalable and efficient data solutions. Your role will encompass various responsibilities, including but not limited to the following:

Key Responsibilities:

1. Solution Design: Architect end-to-end data science solutions, including data ingestion, processing, modeling, and deployment, to address specific business problems.

2. Technical Leadership: Provide technical leadership and guidance to data scientists and engineers, ensuring alignment with best practices and industry standards.

3. Data Strategy: Collaborate with stakeholders to define data strategies, including data acquisition, data transformation, and data governance.

4. Model Development: Lead the development of machine learning, Generative AI and statistical models to extract insights and predictions from data.

5. Scalability: Design solutions that can scale to handle large volumes of data and evolving business needs.

6. Cloud Platforms: Work with cloud platforms such as AWS, Azure, or GCP to design and implement data solutions.

7. Data Integration: Integrate data from various sources, both structured and unstructured, to create comprehensive datasets for analysis.

8. Security and Compliance: Ensure data security and compliance with data privacy regulations throughout the data science process.

9. Documentation: Maintain detailed documentation of architecture, data flows, and best practices.

10. Continuous Learning: Stay up-to-date with the latest advancements in data science, machine learning, and AI technologies.

Qualifications:

1. Bachelor's or masters degree or Ph.D. in computer science, data science, or a related field.

2. Proven experience as a Data Science Solution Architect or a similar role.

3. Strong knowledge of data science, machine learning, and artificial intelligence concepts.

4. Proficiency in programming languages such as Python, R, or Java.

5. Experience with data visualization tools, libraries, and frameworks.

6. Familiarity with big data technologies (e.g., Hadoop, Spark) and cloud platforms.

7. Excellent problem-solving and communication skills.

8. Ability to work collaboratively in cross-functional teams.

9. Relevant certifications (e.g., AWS Certified Solutions Architect, Microsoft Certified: Azure Solutions Architect) a plus.