Principal Consultant - Data Science

Passion for technology drives us, but it’s innovation that defines us. From design to development and support to management, Neudesic offers decades of experience, proven frameworks and a disciplined approach to quickly deliver reliable, quality solutions that help our customers go to market faster.

What sets us apart from the rest, is an amazing collection of people who live and lead with our core values. We believe that everyone should be Passionate about what they do, Disciplined to the core, Innovative by nature, committed to a Team and conduct themselves with Integrity. If these attributes mean something to you - we'd like to hear from you.

Neudesic is currently seeking a Principal Consultant - Data Science for our Data & AI Practice. This role requires the perfect mix of being a brilliant technologist and also a deep appreciation for how technology drives business value. The ideal candidate will have broad and deep expertise across modern technologies and the ability to design end-to-end solutions that align with client business objectives.

You should be adept at engaging senior level technology leaders and driving conversations around data management, real-time and predictive analytics, and data visualization to deliver measurable business value.

To be successful, you will demonstrate strong leadership and relationship‑building skills, earning the trust of both clients and technical teams. You will drive the successful delivery of machine learning and deep learning initiatives, partnering closely with solution architects to ensure client outcomes through your expertise in data management and visualization solutions. Collaborating with a diverse sales organization, you will represent Neudesic's capabilities and success stories, while contributing to the creation of sales assets that effectively communicate our value proposition to technical and business stakeholders.

• Minimum of 8+ years of professional experience in data science, advanced analytics, or related technical fields
• 3+ years of hands‑on experience in building and deploying machine learning and/or generative AI solutions in a business environment
• Bachelor’s degree in a quantitative field (e.g., Computer Science, Engineering, Business Analytics, Statistics, Mathematics); advanced degree preferred
• Proven expertise with Databricks and Microsoft Fabric for data engineering and ML workflows
• Proficient in Python (including ML/AI libraries such as scikit‑learn, PyTorch, Tensor Flow) and SQL; experience with R is a plus
• Strong understanding of statistical modeling, machine learning, MLOps, and large language models (LLMs)
• Demonstrated ability to work with large, complex datasets and relational/cloud‑based databases (e.g., Azure SQL, Delta Lake)
• Experience operationalizing ML models and pipelines in production environments

• Experience with cloud platforms, especially Microsoft Azure (Azure Machine Learning, Azure Data Lake, etc.)
• Familiarity with modern data engineering tools and orchestration frameworks (e.g., dbt, Apache Airflow, Delta Live Tables)
• Experience with data visualization and BI tools (e.g., Power BI, Tableau)
• Ability to communicate complex analytical and technical concepts to both technical and non‑technical audiences
• Strong data storytelling skills and ability to drive insights from exploratory and visual analysis
• Intellectual curiosity, strong problem‑solving skills, and a collaborative, team‑oriented mindset
• Demonstrated leadership, relationship‑building, and mentoring abilities

The digital business uses data as a competitive differentiator. The explosion of big data, machine learning and cloud computing power creates an opportunity to make a quantum leap forward in business understanding and customer engagement. The availability of massive amounts of information, massive computing power and advancements in artificial intelligence allow the digital business to more accurately predict, plan for and capture opportunity unlike ever before.

The Data & AI service line represents the evolution from using data solely for reporting past events to harnessing…