Forvis Mazars

Description & Requirements

How You Will Contribute:

• Engage with clients professionally and consistently to understand their needs, deliver effective solutions, and build trust.
• Assess clients' data strategy, systems, and datasets by exploring cloud and on-premise infrastructure and conducting meetings.
• Contribute to the design and development of data products, leading aspects of technical design.
• Conduct exploratory data analysis, develop predictive models, and create pipelines that generate reliable outputs for visualizations and applications.
• Set a high standard for quality and performance in code development, ensuring integration with CI pipelines through functional and unit testing.
• Take on leadership responsibilities in client projects as technical expertise grows.
• Provide training and mentorship to team members and interns as needed.
• Lead and support client engagement throughout all phases of the project lifecycle.

We are looking for people who have Forward Vision and:

• Strong problem-solving abilities, particularly in product development.
• Motivation to drive business impact through data-driven strategies.
• Comfort working with diverse stakeholders and teams.
• Excellent written and verbal communication for cross-team collaboration.
• Enthusiasm for learning and mastering new technologies in a fast-evolving environment.
• Ability to travel occasionally
• Ability to support and engage with users of data and analytics products.

Minimum Qualifications:

• Bachelor's degree in a Data & Analytics-related field (or equivalent experience), such as Data Science or Data Engineering.
• Knowledge of cloud architecture, system design, and integration.
• Expertise in developing and deploying data models and products in production environments.
• Proficiency in data visualization tools such as PowerBI or Tableau.
• Experience with text mining, NLP, or Generative AI.
• Background in solution architecture, DevOps, or data engineering with platforms like Azure, AWS, or GCP.
• Strong proficiency in Python, Pandas, Pyspark, and scikit-learn.
• Understanding of various machine learning techniques, including clustering, decision trees, boosting, and neural networks.
• Familiarity with Linux ecosystems, including Bash, SSH, virtualization, and automation.
• Experience with databases such as SQL and Spark-based platforms (Databricks, Fabric, Synapse).
• Comfort working with Git for Continuous Integration and Deployment (GitLab CI, Docker Compose).
• Strong command of data exploration and analysis in an open-source environment.
• Proven ability to develop and implement machine learning models and simulations.
• Experience in data visualization (preferred).

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