Senior Analytics Consultant

Wells Fargo Bank•Charlotte, NC

17h•Onsite

About The Position

Wells Fargo is seeking a Senior Analytics Consultant focused on data analytics and automation solutions supporting forecasting, risk identification, and operational decisioning. This role is strongly oriented toward business-domain data capture and automation, with a primary focus on exploring and analyzing large volumes of unsecured lending collections operations data. The consultant will own the end-to-end analytics lifecycle for Unsecured Lending Operations use cases—including data discovery, business-logic translation to technical development and developing forecast models for collection and recovery business arena —while leveraging machine learning, NLP, and AI techniques where appropriate to enable scalable automation. The role emphasizes hands-on execution, rapid prototyping, and disciplined evaluation to ensure outputs are reliable, explainable, and aligned to business needs.

Requirements

4+ years of Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
Advanced SQL proficiency required (SAS, Teradata, Snowflake)
Strong statistical foundation and practical experience with disciplined evaluation (back-testing, holdout validation, stability and sensitivity testing, and uncertainty quantification), with a focus on producing reliable and explainable outputs for business use.
Strong communication and consulting skills, with the ability to translate analytical results into clear recommendations, trade-offs, and measurable business outcomes for both technical and non-technical stakeholders.

Nice To Haves

Proven ability to develop, validate, and maintain forecasting and predictive models that support operational planning and decisioning (e.g., inventory projections, roll-rate/flow models, recovery forecasting, capacity/staffing planning, and scenario analysis).
Strong hands-on experience with Python-based analytics and automation workflows working with large, complex datasets
Demonstrated experience performing data discovery, source-to-target analysis, and building reusable data transformations (e.g., standardized metrics, dimensional models, or curated analytical datasets) with strong attention to data quality and lineage.
Familiarity with machine learning, NLP, and applied AI techniques, and the judgment to apply them selectively to collections operations problems (e.g., text mining of agent notes, classification/prioritization, segmentation, and anomaly detection).
domain knowledge in unsecured lending collections and recovery (delinquency management, treatment strategies, customer contact operations, loss mitigation, and operational KPIs).

Responsibilities

Lead hands-on data discovery and domain data capture across unsecured lending collections operations, partnering with business and technology teams to identify source systems, define critical data elements, and establish fit-for-purpose datasets.
Design, develop, and enhance forecast models that support staffing/capacity planning, treatment strategy evaluation, and inventory projection; quantify uncertainty and scenario impacts to inform planning decisions.
Explore, profile, and analyze large-scale collections and recovery data (e.g., accounts, payment behavior, delinquency/roll rates, treatment strategies, contact outcomes, operational capacity, and agent performance signals) to surface actionable insights and decision opportunities.
Own the end-to-end analytics lifecycle for prioritized use cases—including problem framing, requirements definition, data preparation, and data analysis output.
Translate collections and recovery business logic (policies, treatment paths, segmentation, and operational constraints) into technical specifications and scalable implementations (SQL/Python workflows, reusable data transformations, and production-ready metrics).
Build and automate risk identification signals for operational decisioning (e.g., early-warning indicators, drift detection, emerging loss pockets, operational bottlenecks), and integrate outputs into business routines and dashboards.
Leverage machine learning, NLP, and AI techniques where appropriate to enable scalable automation—such as text mining of notes, disposition narratives, or correspondence; classification and prioritization; clustering and segmentation; and anomaly detection.
Communicate findings and recommendations clearly to non-technical stakeholders, translating analytical results into operational actions, trade-offs, and measurable business outcomes.
Collaborate cross-functionally with collections leadership, strategy, risk, compliance, and technology partners to align solutions with risk appetite, governance requirements, and real-world operational constraints.
May mentor junior analysts/consultants by sharing best practices in data discovery, forecasting, automation design, and responsible use of ML/AI in operational environments.