Job Details
Data Partner / Practice Lead
2026-04-01 The Dignify Solutions, LLC all cities,AK
Description:
- Extensive experience (10-12+ years) in data science, machine learning, AI, or related domains, with a proven track record of leading successful projects and teams within a system integration context.
- Strong understanding of system integration principles, architectures, and technologies, with the ability to design and implement data science and AI/ML solutions that seamlessly integrate with existing systems and processes.
- Experience in at least one of the industry verticals such as Fintech, Life sciences & Healthcare, Manufacturing, or energy & Utilities is MUST, along with relevant certifications in data science, AI, or related fields.
- Deep working knowledge of Generative AI and the latest market trends and create a roadmap and vision for our clients.
- 4-5 years of experience working as a data science practice leader at Big 4 or boutique consulting firms
- Excellent communication, leadership, and client-facing skills, with the ability to build trusted relationships, influence stakeholders, and articulate complex technical concepts to diverse audiences.
- Strong analytical and problem-solving abilities, with a passion for driving innovation and leveraging data-driven insights to solve business challenges for clients.
- Experience in solutions architecture. technical domains such as AI/ML, multimodal ML, model evaluation, MLOps, MLSecOps, ML training, inference, data engineering, data science, fine-tuning
- Manage and mentor a team of skilled data scientists, fostering a culture of collaboration, innovation, and continuous learning.
- Proficiency in programming languages such as Python, R, and Java, along with experience with data and AI libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Take the lead in designing the AI architecture and selecting technologies from both open-source and commercial offerings.
- Knowing the workflow and pipeline architectures of ML and deep learning workloads, including the components and trade-offs across data management, governance, model building, deployment, and production workflows, is crucial.
- Experience in advanced analytics tools (Python, R) along with applied mathematics, ML, Deep Learning frameworks (such as TensorFlow), and ML techniques (such as random forest and neural networks).
- Experience in Machine Learning solutions (using various models, such as Linear/Logistic Regression, Support Vector Machines, Deep Neural Networks,..)
- Developing AI and ML models in real-world environments and integrating AI and ML using cloud-native or hybrid technologies into large-scale enterprise applications.