• Designing model evaluation pipelines for models in development and production • Designing user studies for subjective model evaluations. • Converting requirements into measurable metrics. • Designing and developing automated evaluation dashboard to see model performances and compare results. • Training new models to capture new and different evaluation metrics. • Communicating with the model team to help design better models based on the evaluation results. • Communicating with the data team to help decide the type of data necessary to improve model performance. • Communication with the product-manager to make sure product requirements are correctly measured. • Help grow the evaluation team as the founding member. • Lead the evaluation team in the future.

MIS - Analytics Engineer Virtual• United States Job Description: General Description: Phillips & Cohen Associates Ltd. is an award-winning probate estate and debt settlement industry leader. We are seeking an Analytics Engineer to support Strategy & Analytics function here at PCA and help identify opportunities that drive performance. The ideal candidate should demonstrate strategic curiosity, challenge assumptions and translate analysis into insights. This role combines analytics engineering, data modeling, automation, and applied analytics to transform data into actionable insights, connect data analysis to execution to accelerate growth and competitive advantage. The Analytics Engineer plays critical role in turning data into strategic asset – ensuring the business can rely on accurate, timely insights to drive performance and growth. Responsibilities: Support analytics use cases (segmentation, forecasting, A/B testing). Develop, implement and optimize debt recovery processes, including dialer, scoring and portfolio segmentation. Engineer new data assets and derived datasets when required information is not available in existing source systems. Develop scalable data models and schemas to support portfolio valuation, forecasting, reporting, and operational analytics. Eliminate conflicting metrics and definitions, creating a single source of truth across teams. Build and maintain automated end-to-end analytics workflows, including scheduling, orchestration, alerts, and reporting. Implement data quality rules, validation logic, automated QA checks, and monitoring frameworks to ensure data integrity. Perform exploratory data analysis to identify trends, data gaps, and metric behavior related to portfolio performance. Translate business questions and strategy needs into technical requirements, datasets, and analytical solutions. Create reports, dashboards, and analytical outputs that are usable and interpretable by non-technical stakeholders. Optimize SQL queries and ETL/ELT processes for performance, accuracy, and reliability. Collaborate closely with analytics, engineering, and business partners to align data definitions, metrics, and logic. Document data pipelines, models, transformations, business rules, and data dictionaries to support governance and continuity, ensuring consistent metric definition across the organization. Qualifications: Minimum of 3 years of experience in analytics engineering, data engineering, data analytics, or similar hybrid analytics roles. Advanced SQL skills with hands-on experience building scalable transformations and data models. Proficiency in programming language such as SAS, Python or R for analytics, automation, and modeling. Demonstrated experience designing datasets and data architecture to support advanced analytics and forecasting. Experience with BI and visualization tools such as Power BI, Tableau, or Looker. Strong analytical thinking with the ability to translate complex data into actionable business insights. Ability to work effectively in ambiguous, open-ended problem spaces. Understanding of statistical concepts and predictive modeling techniques. Strong written and verbal communication skills with both technical and non-technical audiences. Experience developing SQL Server jobs, stored procedures, triggers, or similar database objects. Familiarity with Git or version control systems. Background in non-performing debt industry, financial services, portfolio analytics, forecasting, or operations analytics is a plus.

• Design and deploy machine learning algorithms for use cases spanning e-commerce, consumer trends, markets, logistics, and new products • Work on real-world consumer data for natural language processing, image classification, time series analysis, outlier detection, user modeling, etc • Work with large unstructured data • Be at the intersection of mathematics, machine learning, business, and computer science and impact millions of users through your work • Multiply the effect of your data science and data team members by building frameworks, tools, and methodologies that the entire team use • Provide thought leadership to a team through high quality write-ups, reviews, and a strong vision grounded in practical experience and a wider industry view

• Design, build, and improve ASR, audio, and speech-related ML systems for production. • Develop signal processing pipelines for noisy, compressed, telephony-style, or real-world audio. • Train, fine-tune, evaluate, and deploy models for ASR, audio classification, diarization, redaction, or related tasks. • Own ML workflows end-to-end: data preparation, model training, validation, inference, monitoring, and iteration. • Optimize inference for latency, throughput, cost, and reliability. • Debug model quality issues through data analysis, targeted evaluations, and production monitoring. • Collaborate with product and engineering teams to turn business problems into practical ML solutions.