Job Title: Data Science / AI & ML Practice Lead
Job Location: NJ (Onsite)
Work Authorization: Open
Mode of Interview: Phone/Skype
Job Duration: FTE
Strategic Leadership:
Develop and execute a strategic roadmap for the data science and AI/ML practice, aligning with the firm's overall objectives and market trends.
Identify opportunities for innovation and differentiation, leveraging data science and AI/ML to solve complex business problems and create value for Birlasoft clients.
Qualifications:
Advanced degree (Ph.D. or Master's) in Computer Science, Statistics, Mathematics, Engineering, or related field.
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 science and AI/ML 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.
Experience in developing best practices for the ML life cycle, feature engineering, model management, MLOps, deployment, and monitoring.