Principal Data/Solution Architect

Project / Task : A key player in shaping our data science strategies and driving innovative solutions. This role requires a seasoned professional with a deep understanding of data science, exceptional leadership skills, and the ability to architect and deliver cutting-edge data-driven solutions. Join us to lead the

charge in transforming data into actionable insights and driving business success through strategic technical leadership.

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.

Job Profile

Brief description)

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.

years of Experience 15-20

Technology Data Bricks, Domino Data, Palantir AWS, Azure, GCP, Data

Modeling, Data Architecture, ML / AI / LLM, SnowFlake,

OpenAI, LAMA3

Skill Set 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.