About OneSix OneSix is a leading data and artificial intelligence (AI) consultancy that helps businesses build the strategy, technology, and teams they need to scale growth and efficiency. Its team of skilled Data Engineers, Data Scientists, Machine Learning (ML) Experts, and AI Engineers seamlessly integrate with client teams to solve their most challenging business problems. Leveraging strategic partnerships with Snowflake, AWS, Matillion, Fivetran, Pyramid Analytics, and more, the company uses modern technology, scalable architectures, and industry best practices. With the recent acquisition of Strong Analytics, an ML and AI consultancy, OneSix is a uniquely powerful business partner to the enterprise, with a talent mix that is nearly impossible to find under one roof. OneSix is a fast-growing firm with significant career opportunities for motivated professionals who want to help create a unique company. We are committed to fostering an inclusive employee experience that reflects the world we live in today. We’re an equal-opportunity employer that welcomes people regardless of backgrounds, experiences, abilities, and perspectives. Job Description and Responsibilities: The Lead Machine Learning Scientist at OneSix works collaboratively to design and develop machine-learning-based solutions for clients. This role is mission-critical, leading project teams, shaping project scopes, and working directly with clients to iterate on solutions. While not a direct people manager, the Lead ML Scientist serves as a technical team lead, providing mentorship, guidance, and strategic direction to team members to ensure successful project delivery. They are responsible for bringing together state-of-the-art solutions to solve our clients' most pressing problems. - Conduct research and development on AI/ML models to solve complex technical challenges. - Design, train, and optimize machine learning models for performance, scalability, and efficiency. - Experiment with state-of-the-art AI techniques, such as deep learning, reinforcement learning, and generative models. - Work closely with engineers and scientists to integrate models into production environments. - Analyze and preprocess large datasets to improve model accuracy and robustness. - Stay updated with advancements in AI and ML and apply new findings to ongoing projects. - Ensure AI models adhere to ethical AI practices, fairness, and interpretability principles. - Contribute to technical documentation and research publications when applicable. - Strong problem-solving skills and analytical thinking. - Ability to work independently and collaborate with cross-functional teams. - Effective communication skills for discussing technical concepts and findings. - Act as a technical lead by guiding and mentoring team members, providing feedback, and fostering collaboration. - Help shape project scopes, drive technical discussions, and ensure alignment with business goals. Experience / Qualifications - Master’s or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field. - 6+ years of hands-on AI/ML experience, with increasing leadership responsibilities. Technical Skills: - Strong proficiency in ML tools (e.g., PyTorch, Scikit-learn, Huggingface). - Advanced programming skills in Python; experience with R and/or C++ is a nice to have. - Deep understanding of machine learning algorithms, statistical modeling, and optimization techniques. - Experience scoping and leading machine learning projects from scoping/design through deployment/monitoring. - Experience deploying ML models in production environments. - Experience with cloud-based ML infrastructure (AWS, GCP, Azure) and MLOps practices/tools. Leadership & Soft Skills: - Strong mentorship abilities and a passion for helping others grow. - Ability to influence technical strategy without direct managerial authority. - Effective communication skills to present complex ML concepts to diverse audiences. - Problem-solving mindset and ability to work cross-functionally with different teams. Compensation / Benefits - Competitive compensation - Company-paid medical, vision, dental, and wellness benefits for employees - Company-provided home office equipment - Flexible vacation and sick days - Team-oriented and supportive working environment - Company-sponsored events and swag OneSix provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, familial status, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

EBSCO Information Services (EBSCO) delivers a fully optimized research experience, seamlessly integrated with a powerful discovery platform to support the information needs and maximize the research experience of our end-users. Headquartered in Ipswich, MA, EBSCO employs more than 2,700 people worldwide, with most embracing hybrid or remote work models. As an AI-enabled service leader, we thrive on innovation, forward-thinking strategies, and the dedication of our exceptional team. At EBSCO, we’re driven to inspire, empower and support research. Our mission is to transform lives by providing reliable and relevant information — when, where and how people need it. We’re seeking dynamic, creative individuals whose diverse perspectives will help us achieve this global, inclusive mission. Join us to help make an impact. Your Opportunity As a Senior ML Ops Engineer 1, you will play a key role in designing, building, and maintaining production-grade machine learning (ML) pipelines and infrastructure within our AWS-based data lakehouse ecosystem. Working alongside data engineers, data scientists, and DevSecOps teams, you will operationalize ML models and ensure the reliability, security, and scalability of the ML lifecycle—from data ingestion through training, deployment, and monitoring. You will help shape the ML Ops framework, contribute to automation that accelerates delivery, and ensure alignment with established platform Non-Functional Requirements (NFRs). This is a highly collaborative, hands-on engineering role requiring a deep understanding of AWS services, automation, and ML workflow orchestration. This position is remote and operates within a distributed agile environment. What You'll Do - Design, build, and maintain ML Ops pipelines supporting model training, validation, and deployment across AWS environments. - Implement automation for model packaging, testing, deployment, and monitoring using CI/CD best practices. - Collaborate with data engineers and data scientists to operationalize ML workloads within the data lakehouse ecosystem. - Develop and maintain integrations between data ingestion, feature stores, and model repositories. - Apply infrastructure-as-code (Terraform, AWS CDK, CloudFormation) to automate ML pipeline infrastructure. - Implement and manage model versioning, reproducibility, and lineage tracking using tools such as MLflow or SageMaker Model Registry. - Define and automate monitoring, alerting, and retraining strategies for deployed models. - Ensure all ML infrastructure and pipelines meet enterprise security, compliance, and governance standards. - Participate in code reviews, knowledge sharing, and continuous improvement of ML Ops practices. - Mentor junior engineers and contribute to documentation, standards, and best practices for ML Ops across teams. Your Team: This role is part of the Data & AI organization, focusing on the operationalization of ML models and pipelines within AWS. Areas of specialty include: - ML pipeline automation and orchestration - Model versioning, governance, and observability - Feature store integration and reproducibility - Secure, compliant, and scalable ML infrastructure - Continuous improvement of ML lifecycle automation About You - Bachelor's Degree in Computer Science, Data Engineering, or a related technical field or equivalent experience. - 4+ years of professional experience in software, data, or ML engineering. - 2+ years of direct experience implementing and maintaining ML pipelines in production. - Strong proficiency in Python and familiarity with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn. - Hands-on experience with AWS services (SageMaker, Step Functions, Lambda, ECR, S3, Glue, IAM). - Solid understanding of CI/CD, containerization (Docker) - Experience with building CI/CD pipelines (Jenkins, Github Actions, etc.). - Experience with infrastructure-as-code and automation (Terraform, AWS CDK, or CloudFormation). - Strong understanding of data pipelines, ETL/ELT concepts, and feature engineering in a lakehouse environment. - Proven ability to apply software engineering practices to machine learning workflows. - Strong communication and collaboration skills across multidisciplinary teams. What sets you apart: - Experience with feature stores, data catalogs, and metadata management. - Familiarity with model governance and compliance frameworks. - Experience with model monitoring and drift detection tools (CloudWatch, or custom solutions). - Understanding of data lakehouse technologies such as Apache Iceberg or Delta Lake. - Contributions to open-source ML Ops or DevOps tooling. - Experience in Agile development environments and cross-functional collaboration. Pay Range USD $120,120.00 - USD $171,600.00 /Yr.

• Build and deploy NLP models to analyze news, social media (Twitter/X, Discord), and Reddit to gauge market sentiment for stocks and crypto assets. • Develop systems to identify and extract entities (tickers, company names, wallet addresses, transaction IDs) from unstructured financial documents and chat logs. • Create pipelines to parse and extract data from financial statements, whitepapers, and regulatory filings (e.g., SEC filings) to assist in automated research. • Implement NLP techniques to analyze transaction metadata and communication patterns to identify potential money laundering (AML) or fraudulent payment activity. • Build or fine-tune LLMs (Large Language Models) to power specialized chatbots capable of answering complex queries about portfolio performance, crypto protocols, or trading rules. • Optimize internal search engines using semantic search and embeddings to help users find relevant financial instruments or transaction history. • Manage the full MLOps lifecycle, including data labeling for financial jargon, model training, deployment via APIs, and monitoring for "model drift" in volatile markets.

• Design, train, and ship machine learning models that power recommendations on the Now Playing View for hundreds of millions of users • Own ranking systems end-to-end, from experimentation and training pipelines to online serving and monitoring • Build and iterate on generative and agentic ML approaches to improve session steering and cross-content discovery • Work in an AI-native development environment, using AI tools to accelerate development while applying strong engineering judgment • Run A/B experiments, define success metrics, and translate improvements into measurable user impact • Collaborate closely with engineers, data scientists, researchers, and product managers to bring ideas into production • Shape the ML roadmap by identifying high-impact opportunities and mentor teammates