BRG is a company specializing in providing subject matter expertise backed by data analytics in areas including economics, mergers, litigation, damages, investment, healthcare-related insights, and others.
Expert Solutions team is the liaison between business customers and IT for non-commodity IT services delivering solutions to complex business and technical problems. The goal of the Expert Solutions team is to be the focal point of technical excellence and technological innovation within BRG to help our business teams be more effective and enhance competitive advantage. Expert Solutions is a highly collaborative team responsible for designing, developing, modifying, adapting, and implementing short- and long-term solutions to information technology (IT) needs through new and existing data applications, systems architecture, network systems and data architecture.
The mid-level AI/ML Engineer position requires both technical and industry-specific knowledge, with desired skills that indicate familiarity with leading-edge technologies and platforms in the field of data analytics and AI/ML. It also requires strategic knowledge of the cloud computing technology and providers, including Platform as a Service (PaaS), Software as a Service (SaaS), and/or Infrastructure as a Service (IaaS), and the skills to configure those clouds to fit company needs. Azure, AWS, and GCP experience is a plus. The position requires analysis skills to understand and capture the business users’ AI/ML and related data analytics requirements by working closely with business Experts, IT teams, and vendors.
- Director of Information Technology & Expert Solutions
- This role interfaces with leaders and designees of Expert Communities (practices) throughout the firm.
- Additionally, this role would interface heavily with internal IT staff as well as IT business partners.
Major Responsibilities/ Job Functions:
- Collaboration with Business and Expert Solutions Team:
- Engage closely with business users and other members of the Expert Solutions team to assess and define AI/ML and big data analytics requirements.
- Utilize a deep understanding of AI/ML applications to translate business needs into technical solutions.
- Development of Advanced Analytics and ML Models:
- Create and demonstrate sophisticated analytics programs and machine learning models to address complex business challenges.
- Apply statistical methods and data-driven techniques to uncover insights and drive business decisions.
- Integration of AI/ML Technologies into Existing Workflows:
- Integrate new AI/ML software and data management technologies into current business and data workflows.
- Assist the transition and adoption of new technologies by providing technical guidance and support.
- Building and Managing Data Systems and ML Pipelines:
- Design and build efficient data systems and pipelines optimized for machine learning, supporting business users in data ingestion, organization, and storage.
- Implement tools and processes for managing large-scale datasets used in AI/ML projects.
- Development and Maintenance of Data and AI Architectures:
- Develop, construct, test, and maintain architectures that support data science and machine learning projects.
- Regularly review and realign architectures to ensure they meet evolving business requirements and industry best practices.
- Research and Evaluation of AI/ML Tools and Technologies:
- Conduct ongoing research to stay abreast of the latest advancements in AI/ML tools and technologies.
- Evaluate and recommend tools that enhance the organization’s AI/ML capabilities.
- Vendor and Consulting Firm Engagement for AI/ML Solutions:
- Assist in the selection and management of external vendors and consulting firms for AI/ML-related projects.
- Guide and oversee external partners in the evaluation and implementation of AI/ML solutions, ensuring alignment with business objectives.
- Ensuring Compliance and Ethical AI Practices:
- Adhere to data privacy and security regulations.
- Promote ethical AI practices in all machine learning and data analytics projects.
Knowledge, Skills, and Behaviors - Required
- Educational Background:
- A Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, or a related field, with a specialization in machine learning, artificial intelligence, or data science.
- Work Experience:
- 3-5 years of relevant experience in AI/ML, with a proven track record in applying machine learning techniques to real-world problems. Experience in data analytics in sectors like economics, healthcare, investment, or similar fields is highly desirable.
- Technical Skills:
- Proficient in programming languages such as Python, R, or Java.
- Expertise in machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn, NLTK).
- Advanced knowledge of machine learning algorithms, including supervised and unsupervised learning, deep learning, NLP, and predictive modeling.
- Experience with Retrieval-Augmented Generation (RAG) for enhancing language models with external knowledge sources.
- Demonstrated experience in fine-tuning pre-trained models on domain-specific data to improve performance and accuracy.
- Hands-on experience with Large Language Models like GPT-3 or GPT-4, including using APIs like ChatGPT for integration into applications or services.
- Experience in configuring and managing self-hosted LLMs such as LLAMA, including setup, maintenance, and optimization for specific use cases.
- Data Analytics and Processing Skills:
- Advanced Data Handling: Exceptional skills in managing, cleaning, and preprocessing large and complex datasets using advanced SQL techniques for complex queries, and expertise in using Python's Pandas library for data manipulation and analysis.
- Data Processing Frameworks: Proficiency in using big data processing frameworks like Apache Spark, which enables handling of large-scale data processing and analytics over distributed systems.
- Econometrics and Statistical Modeling: Deep understanding of econometrics methods, including time-series analysis, regression models, and hypothesis testing, to analyze economic data. Familiarity with statistical modeling techniques and their application in real-world scenarios, using tools like R or Python’s statistical libraries.
- Quantitative Methods: Expertise in applying quantitative methods like linear programming, optimization techniques, and probability theory in solving complex problems.
- Domain Expertise:
- Economics: Understanding of economic principles and models, and how AI/ML can be leveraged for economic forecasting, market analysis, and policy evaluation.
- Healthcare: Knowledge of healthcare systems, electronic health records (EHR), patient data analysis, and application of AI for predictive diagnostics, treatment personalization, and drug discovery.
- Litigation and Legal Analytics: Familiarity with the legal domain, including litigation processes, document analysis, and the use of AI for case prediction, legal research, and contract analysis.
- Project Management and Teamwork Skills:
- Ability to manage AI/ML projects effectively, coordinate with cross-functional teams, and deliver results within set timelines.
- Communication Skills:
- Excellent communication skills to explain complex AI/ML concepts to non-technical stakeholders and collaborate effectively within the organization.
- Problem-Solving and Innovative Thinking:
- Strong analytical and problem-solving skills, with an ability to think creatively to provide innovative solutions to complex problems.
- Commitment to Continuous Learning:
- Dedication to staying updated with the latest developments in AI/ML, particularly in areas like RAG, fine-tuning, and LLMs.
- Ethical and Compliance Awareness:
- Knowledge of ethical considerations in AI, and compliance with legal and regulatory standards, especially in handling sensitive data in areas like healthcare and finance.
Knowledge, Skills, and Behaviors - Desired
- Major Vendor SaaS Data Analytics/AI-ML Solutions:
- Databricks: Experience with Databricks’ unified analytics platform for massive-scale data engineering and collaborative data science.
- Snowflake: Knowledge of Snowflake’s cloud data platform for data warehousing, data lakes, data engineering, data science, and developing data applications.
- Google BigQuery: Proficiency in using Google BigQuery for big data analytics, including its ML capabilities for building and operationalizing machine learning models.
- Microsoft Azure Fabric (formerly Azure Synapse Analytics): Experience with Azure Fabric / Synapse Analytics for integrating various analytics services and managing big data and data warehouse solutions.
- Amazon Web Services (AWS): Familiarity with AWS’s suite of AI/ML services, such as Amazon SageMaker for building, training, and deploying machine learning models at scale.
- Cloud Computing Expertise and Strategy:
- Strategic Cloud Knowledge: Strong understanding of cloud computing technologies and their strategic application in business. This includes familiarity with various service models such as Platform as a Service (PaaS), Software as a Service (SaaS), and Infrastructure as a Service (IaaS).
- Cloud Configuration Skills: Proven ability to configure cloud environments according to company needs, ensuring efficient and secure utilization of cloud resources. This includes setting up scalable and robust cloud infrastructures, optimizing cloud services for cost and performance, and ensuring compliance with data protection and privacy standards.
- Experience with Major Cloud Providers: Hands-on experience with leading cloud platforms such as Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). This includes deploying and managing applications and services, utilizing cloud-native AI/ML tools, and integrating cloud services with on-premise systems.
- Business and IT Collaboration: Strong analytical skills to understand and capture big data and data analytics requirements from business users. Ability to work closely with business experts, IT teams, and vendors to align cloud solutions with business objectives.
- Solution Design and Implementation: Competence in designing and implementing cloud-based solutions for data analytics and machine learning projects. This involves selecting the right cloud services and tools, creating data pipelines, and ensuring that the solutions are scalable, secure, and cost-effective.
- Cross-Platform Integration: Skills in integrating various cloud services and platforms to create cohesive and efficient systems that leverage the strengths of each cloud provider.
- Cloud Security and Compliance:
- Knowledge of cloud security principles and practices, as well as experience in ensuring compliance with regulatory requirements in a cloud environment.
Candidate must be able to submit verification of his/her legal right to work in the U.S., without company sponsorship.
Salary Range: 115-160k