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Credit Risk Data Science Jobs in Camden, NJ (NOW HIRING)

Position Details Position Information Recruitment/Posting Title Lecturers, Data Science Department Mathematics Salary Details A minimum of $2,777 per credit Offer Information The final salary offer ...

Analyst, AI Risk Management

Radnor, PA · Hybrid

$72K - $131K/yr

... risk, data science, or a related quantitative filed that directly aligns with the specific responsibilities for this position. Application Deadline Applications for this position will be accepted ...

Analyst, AI Risk Management

Radnor, PA · Hybrid

$72K - $131K/yr

... risk, data science, or a related quantitative filed that directly aligns with the specific responsibilities for this position. Application Deadline Applications for this position will be accepted ...

Analyst, AI Risk Management

Radnor, PA · On-site

$72K - $131K/yr

... risk, data science, or a related quantitative filed that directly aligns with the specific responsibilities for this position. Application Deadline Applications for this position will be accepted ...

Position Details Position Information Recruitment/Posting Title Lecturers, Data Science Department Prevention Science Salary Details A minimum of $2,777 per credit. Offer Information The final salary ...

The role serves as a trusted risk partner to Relationship Management, providing sound credit ... reports and related data; to communicate effectively with customers, management, and staff ...

Lead Data Scientist

Chadds Ford, PA · On-site +1

$144K - $250K/yr

Advanced machine learning modeling and/or technical expertise in developing market differentiation data science products. * Experience in bank card/credit card business, consulting, retail, marketing ...

Showing results 41-60

Credit Risk Data Science information

See Camden, NJ salary details

$37.3K

$114.9K

$199.3K

How much do credit risk data science jobs pay per year?

As of Aug 7, 2026, the average yearly pay for credit risk data science in Camden, NJ is $114,893.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,200.00 and $141,700.00 per year, depending on experience, location, and employer.

How does a credit risk data scientist typically collaborate with other teams within a financial institution?

Credit Risk Data Scientists often work closely with credit analysts, risk managers, and IT professionals to develop, validate, and implement models that assess borrower risk. They frequently participate in cross-functional meetings to translate complex analytical findings into actionable business insights. Collaboration with compliance and regulatory teams is also common to ensure that risk models meet current regulatory standards. Effective communication and teamwork are essential, as the role bridges technical model development and practical risk management decisions.

What is credit risk data science?

Credit Risk Data Science is a specialized field that uses statistical analysis, machine learning, and data modeling techniques to assess and predict the likelihood that a borrower will default on a loan or credit obligation. Professionals in this field analyze large datasets from financial transactions, credit reports, and market trends to develop models that help financial institutions make informed lending decisions. Their work helps manage risk, set appropriate interest rates, and comply with regulatory standards. By leveraging advanced analytics, credit risk data scientists play a crucial role in minimizing losses and maximizing profitability for banks and lenders.

What skills and qualifications are needed to thrive as a credit risk data scientist?

To thrive as a Credit Risk Data Scientist, you need strong analytical skills, proficiency in statistical modeling, and a solid background in finance, mathematics, or a related field, often supported by an advanced degree. Familiarity with programming languages like Python or R, experience with machine learning frameworks, and knowledge of credit risk modeling tools such as SAS or SQL are typically required. Critical thinking, attention to detail, and effective communication are vital soft skills for interpreting data and collaborating with stakeholders. These abilities are crucial for building accurate risk models, informing strategic decisions, and ensuring regulatory compliance in financial institutions.
What are popular job titles related to Credit Risk Data Science jobs in Camden, NJ? For Credit Risk Data Science jobs in Camden, NJ, the most frequently searched job titles are:
What cities near Camden, NJ are hiring for Credit Risk Data Science jobs? Cities near Camden, NJ with the most Credit Risk Data Science job openings:
Infographic showing various Credit Risk Data Science job openings in Camden, NJ as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $114,893 per year, or $55.2 per hour.

Principal Scientist, Data Science

Jj

Spring House, PA

Full-time

Retirement, PTO

Posted 8 days ago


Job description

At Johnson & Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,where treatments are smarter and less invasive, andsolutions are personal.Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.Learn more at jnj.com

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function:

Data Analytics & Computational Sciences

Job Sub Function:

Data Science

Job Category:

Scientific/Technology

All Job Posting Locations:

Cambridge, Massachusetts, United States of America, Raritan, New Jersey, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America

Job Description:

J&J Innovative Medicine - Data, Data Science, and AI - Global Development (DDSAI GD) is recruiting a Principal Scientist. The ideal candidate will Lead analytics, ML, optimization, and GenAI that rely primarily on realworld data to inform clinical trial design, feasibility, and execution facilitation, translate insights from RWD sources (e.g., EHR, claims, registries, digital health) into clear recommendations that shape protocol decisions and operational plans.

J&J Innovative Medicine develops treatments that improve the health of people worldwide. Research and development areas encompass oncology, cardiovascular and metabolic disorders, immunology, pulmonary hypertension, neuroscience, and infectious disease. Our goal is to help people live longer, healthier lives. We have produced and marketed many first-in-class prescription medications and are poised to serve the broad needs of the healthcare market - from patients to practitioners and from clinics to hospitals. To learn more about J&J Innovative Medicine, visit https://www.jnj.com/innovative-medicine

Key responsibilities:

  • Use RWD to quantify disease prevalence, care pathways, and the impact of inclusion/exclusion criteria; produce feasibility scoring across geographies, sites, and subpopulations.

  • Assess RWDfeasible endpoints and proxies; evaluate availability, completeness, quality, and signaltonoise to guide protocol design choices.

  • Construct RWDbased cohorts and external/synthetic controls to benchmark protocol decisions and stresstest samplesize/timeline assumptions.

  • Develop ML and multiobjective optimization solutions primarily powered by RWD to surface tradeoffs (speed, quality, cost, diversity) and recommend design and operational scenarios informed by realworld care patterns.

  • Build RWDcalibrated stochastic simulations of patient journeys to forecast timeline sensitivities and completion risk; provide RWD features, calibration sets, and feasibility constraints to the partner team's enrollment/screenfailure/retention models.

  • Adapt LLMs/GenAI for structured extraction from RWD artifacts (structured and unstructured EHR, notes, radiology/pathology reports, claims, registries); harmonize concepts to standard vocabularies to support eligibility criteria evaluation and scheduleofactivities insights grounded in realworld practice.

  • Clearly communicate RWDbased assumptions, methods, and results to clinical, operational, and leadership stakeholders; coach and mentor colleagues on RWD methodologies, pipelines, and best practices.

Required qualifications:

  • A Ph.D. degree in quantitative discipline (e.g., computer science, electrical and computer engineering, biostatistics, health economics, biomedical informatics, applied mathematics, or similar),

  • 5+ years delivering ML/NLP/GenAI and multi objective optimization solutions with primary reliance on RWD (EHR, claims, registries, digital health), including collaboration with operations analytics teams.

  • Hands on experience with multimodal RWD (structured + unstructured) predictive modeling and stochastic simulations for feasibility and time series scenario forecasting.

  • Demonstrated ability to construct, validate, and deploy models from RWD to inform trial feasibility, endpoint selection, eligibility criteria effects, and external control design.

  • Experience building optimization engines (e.g., evolutionary algorithms, reinforcement learning, mixed integer linear programming) using RWD derived signals to navigate complex tradeoffs.

  • Proficiency in MLOps (e.g., MLflow, Kedro), Git, and CI/CD; strong programming skills in Python and SQL; familiarity with DSPy/LangChain, pymoo, scikit learn, XGBoost, Optuna, PyMC.

  • Familiarity with healthcare privacy/compliance, de identification practices, and RWD data quality management; ability to integrate outputs from operational systems/models when needed while keeping the analytical core RWD driven.

Preferred qualifications:

  • Demonstrated expertise applying RWD methods to inform trial design: target trial emulation, propensity weighting/matching, and survival/timetoevent analyses for endpoint feasibility and external/synthetic controls.

  • Proven collaboration with operations analytics teams by supplying RWDderived cohorts, features, and feasibility evidence that improved enrollment forecasting, site selection, and diversity goals.

Johnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.

Johnson and Johnson is committed to providing an interview process that is inclusive of our applicants' needs. If you are an individual with a disability and would like to request an accommodation, please email the Employee Health Support Center (ra-employeehealthsup@its.jnj.com) or contact AskGS to be directed to your accommodation resource.

#LI-GR1

#LI-Hybrid

#JRDDS

#JNJDataScience

#JRD

Required Skills:

Preferred Skills:

Advanced Analytics, Coaching, Critical Thinking, Data Analysis, Data Privacy Standards, Data Quality, Data Reporting, Data Savvy, Data Science, Data Visualization, Digital Fluency, Econometric Models, Organizing, Process Improvements, Strategic Thinking, Technical Credibility, Workflow Analysis

The anticipated base pay range for this position is :

$117,000.00 - $201,250.00

Additional Description for Pay Transparency:

Subject to the terms of their respective plans, employees are eligible to participate in the Company's consolidated retirement plan (pension) and savings plan (401(k)).
Subject to the terms of their respective policies and date of hire, employees are eligible for the following time off benefits:
Vacation -120 hours per calendar year
Sick time - 40 hours per calendar year; for employees who reside in the State of Colorado -48 hours per calendar year; for employees who reside in the State of Washington -56 hours per calendar year
Holiday pay, including Floating Holidays -13 days per calendar year
Work, Personal and Family Time - up to 40 hours per calendar year
Parental Leave - 480 hours within one year of the birth/adoption/foster care of a child
Bereavement Leave - 240 hours for an immediate family member: 40 hours for an extended family member per calendar year
Caregiver Leave - 80 hours in a 52-week rolling period10 days
Volunteer Leave - 32 hours per calendar year
Military Spouse Time-Off - 80 hours per calendar year
For additional general information on Company benefits, please go to: - https://www.careers.jnj.com/employee-benefits