predictive modeler

RELX Group

<div>About the Business
 
LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti-Money Laundering/Counter Terrorist Financing, Identity Authentication &amp; Verification, Fraud and Credit Risk mitigation and Customer Data Management. You can learn more about LexisNexis Risk at https://risk.lexisnexis.com/
About the Role:
The Sr Statistical Modeler develops and implements analytics and AI solutions that support business and product objectives across LexisNexis Risk Solutions. This role independently executes complex analytical work, translating well defined problem statements into scalable machine learning solutions across the full modeling lifecycle.
About the Team: This team is intentionally AI forward, suited for a practitioner with hands on experience building, operationalizing, and integrating models into production systems. The role collaborates closely with engineering, product, and platform teams and communicates analytical insights to both technical and non technical stakeholders.
Responsibilities:
<ul><li>Applies and integrates statistical, mathematical, predictive modeling and business analysis skills to manage and manipulate complex data from a variety of sources
</li><li>Develops and maintains infrastructure systems that connect internal data sets; creates new data collection frameworks for structured or unstructured data
</li><li>Recognized expert within the function
</li><li>Requires specialized depth and/or breadth of expertise Interprets internal or external business issues and recommends best practices
</li><li>Works independently, with guidance in only the most complex situations
</li><li>Trains/mentors junior staff
</li><li>Serves as an expert of own discipline to clients
</li><li>Interprets internal/external business challenges and recommends best practices to improve products, processes or services
</li></ul>Requirements:
<ul><li>Proven Data Science experience, Advanced academic experience&mdash;such as a Master&rsquo;s degree in a related discipline&mdash;may substitute for part of the required experience
</li><li>Solid experience in applying machine learning and statistical techniques to real‑world problems.
</li><li>Hands‑on experience developing, evaluating, and iterating on predictive and machine learning models.
</li><li>Experience evaluating model performance using appropriate statistical and machine learning metrics and validation techniques.
</li><li>Experience working with structured and unstructured data at scale.
</li><li>Proficiency in Python and/or R using common data science and machine learning libraries (e.g., pandas, NumPy, scikit‑learn, XGBoost, PyTorch).
</li><li>Experience working with SQL and relational or cloud‑based data platforms.
</li><li>Hands‑on experience developing and running data science and AI workloads in cloud environments such as AWS and Azure, including compute, storage, monitoring, and cost‑aware execution.
</li><li>Exposure to modern AI frameworks and tools, including large language model (LLM)&ndash;based solutions and retrieval‑augmented workflows.
</li><li>Experience training, fine‑tuning, or evaluating neural network&ndash;based models as part of applied machine learning solutions.
</li><li>Experience in applying software engineering best practices to data science codebases, including testing, code quality checks, and version control workflows.
</li><li>Ability to independently execute complex analytical work within defined scope.
</li><li>Clear and effective communicator, able to explain technical ideas in a way that&rsquo;s easy for non‑technical audiences to understand.
</li></ul>Learn more about the LexisNexis Risk team and how we work: https://relx.wd3.myworkdayjobs.com/RiskSolutions/page/21c296c982531000b79663f3194b0000
&amp;#xa;&amp;#xa;U.S. National Base Pay Range: $104,900 - $174,700. Geographic differentials may apply in some locations to better reflect local market rates.&amp;#xa;&amp;#xa;Base Pay Range for CO is $104,900 - $174,700. Base Pay Range for IL is $110,100 - $183,500. Base Pay Range for Chicago, IL is $115,400 - $192,200. Base Pay Range for MD is $110,100 - $183,500. Base Pay Range for NY is $115,400 - $192,200. Base Pay Range for New York City is $125,900 - $209,700. Base Pay Range for Rochester, NY is $104,900 - $174,700. Base Pay Range for OH is $99,700 - $166,000. Base Pay Range for NJ is $123,816- $197,784.&amp;#xa;&amp;#xa;&amp;#xa;This job is eligible for an annual incentive bonus.&amp;#xa;&amp;#xa;&amp;#xa;&amp;#xa;&amp;#xa;&amp;#xa;Application deadline is 07/30/2026.&amp;#xa;&amp;#xa;
We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.
We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or please contact 1-855-833-5120.
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Please read our Candidate Privacy Policy .
We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.
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EEO Know Your Rights .</div>

Sr Statistical Modeler

DataAnnotation

<div>
 
 About The Job
 
 
 
 We are looking for existing quantitative professionals to help train AI models. In this role, you will work closely with state-of-the-art AI models on tasks like evaluating AI-generated quantitative analyses, solving technical problems, and providing feedback that directly shapes how these systems reason about data, models, and scientific problems. Whether your background is in data science, astrophysics, economics, biostatistics, operations research, or any other quantitative field, if you think rigorously about data and models, your skills are directly applicable here. Some team members fit this work alongside a full-time role, while others treat it as their primary focus.
 
 
 To get started, once you sign up for an account, you'll take a short assessment (this serves as our version of an interview). If you pass, you'll receive an email confirmation, and paid work will become available on our platform.
 
 
 
 Advantages Of Contracting With Us
 
 
 
 <ul>
 <li>
 You'll be able to choose which projects you want to work on and when
 </li>
 <li>
 You work on your own schedule, on your own computer, from the comfort of your own home
 </li>
 <li>
 Projects are paid hourly starting at $50-$100+ USD per hour, with bonus rates available on some projects
 
 
 </li>
 </ul>
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Evaluate AI-generated quantitative work, including statistical analysis, predictive modeling, scientific reasoning, and data-driven insights, for technical accuracy and real-world validity.
 </li>
 <li>
 Design and solve quantitative problems used to train and benchmark AI systems, spanning areas like forecasting, experimental analysis, optimization, and statistical inference.
 </li>
 <li>
 Write clear technical explanations and well-documented analytical code.
 </li>
 <li>
 Provide feedback that directly shapes the next generation of AI models built for quantitative reasoning.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 2+ years of hands-on experience in a quantitative role or research environment &mdash; such as data science, statistics, economics, finance, physics, biology, epidemiology, operations research, or any adjacent field.
 </li>
 <li>
 Some coding experience required, with comfort writing and reviewing analytical code end-to-end.
 </li>
 <li>
 Practical experience with statistical methods, predictive modeling, and experiment design (e.g., A/B testing, hypothesis testing, regression, classification, time-series forecasting).
 </li>
 <li>
 Fluency in English (native or bilingual level) with strong writing skills.
 </li>
 <li>
 A bachelor's degree in a quantitative field is preferred (Statistics, Computer Science, Mathematics, Engineering, or similar); a master's or PhD is a plus.
 </li>
 <li>
 Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or equivalent demonstrated expertise).
 
 
 </li>
 </ul>
 Note: Payment is made via PayPal. We will never ask for any money from you. PayPal will handle any currency conversions from USD. Only applicants in the United States, Canada, United Kingdom, Ireland, Australia, and New Zealand will be considered for this role. This is an independent contractor position.
 
 
 #datascience
</div>

Experimentation Analyst - AI Trainer

ALICE + OLIVIA

<div>
 
 Location: New York, NY or Secaucus, NJ (Hybrid)
 
 
 Decision Science Engineer (Applied Data Scientist)
 
 
 
 
 
 
 POSITION SUMMARY:
 
 
 
 ALICE + OLIVIA is hiring a Decision Science Engineer to build, operationalize, and own predictive models that improve decision making across Planning, Merchandising/Buying, Customer 360, and Inventory Management. Working primarily within Palantir Foundry as the core analytics and deployment platform, this role bridges data science and platform engineering, translating real-world commercial constraints including seasonality, product lifecycle, allocations, replenishment, promotions/markdowns, and customer behavior into practical, production-grade, human-in-the-loop models.
 
 
 
 
 
 This is a highly cross-functional role with direct, ongoing exposure to business stakeholders and end users, from planners and buyers who consume and act on model outputs, to the teams working within Foundry Workshop applications day-to-day. Understanding how outputs are used in real workflows, and staying close to the people using them, is central to how this role operates. Sitting within the IT organization and serving the full business including DTC, wholesale, planning, and supply chain, this position carries direct visibility to executive business stakeholders. This role is focused on applied data science, decision-support modeling, and model operationalization, not reporting or core data engineering, while partnering closely with Engineering, BI, and the broader Enterprise Applications team.
 
 
 
 
 
 
 RESPONSIBILITIES:
 
 
 <ul>
 <li>
 Design, build, and maintain predictive and statistical models supporting retail planning, buying, inventory, and demand forecasting.
 </li>
 <li>
 Operationalize models within Palantir Foundry using Code Workbook and Code Repository, working against Snowflake as the primary data source and ensuring outputs are production-grade and maintainable.
 </li>
 <li>
 Own and evolve analytics logic and assumptions embedded in planning and buying workflows.
 </li>
 <li>
 Partner with Planning, Buying, Merchandising, and DTC teams to translate business needs into model-driven insights, maintaining direct, ongoing relationships with the stakeholders and end users consuming model outputs.
 </li>
 <li>
 Collaborate with the Enterprise Applications team on Workshop app development to ensure model outputs are surfaced effectively in business-facing Foundry applications.
 </li>
 <li>
 Build models for customer segmentation, CLV, retention/churn, propensity, and campaign targeting (Customer 360).
 </li>
 <li>
 Collaborate with Engineering, who owns data pipelines and Snowflake ingestion, to ensure model inputs are reliable, well-governed, and fit for purpose without owning the underlying data infrastructure.
 </li>
 <li>
 Support human-in-the-loop workflows where planners and buyers can interrogate, override, or adjust model outputs in real time.
 </li>
 <li>
 Validate model outputs against business reality and historical performance, closing the loop directly with end users on model accuracy and relevance.
 </li>
 <li>
 Document methodologies and assumptions to support long-term ownership and institutional knowledge.
 </li>
 </ul>
 
 
 
 
 
 QUALIFICATIONS:
 
 
 <ul>
 <li>
 4 to 8+ years of experience in data science, applied analytics, or predictive modeling.
 </li>
 <li>
 Strong experience building predictive or statistical models including forecasting, optimization, regression, and classification.
 </li>
 <li>
 Proficiency in Python for data analysis, modeling, and production-grade code.
 </li>
 <li>
 Strong SQL and hands-on experience with Snowflake or equivalent cloud data warehouse.
 </li>
 <li>
 Experience with Palantir Foundry strongly preferred, including familiarity with the Foundry Ontology layer, analytics and modeling tooling (Code Workbook, Code Repository), and how model outputs are surfaced through business-facing applications. Candidates who can reason within and contribute to an existing Ontology, and who understand how the semantic data layer connects to model design, will be prioritized.
 </li>
 <li>
 Familiarity with experiment design and causal inference.
 </li>
 <li>
 Experience with time-series forecasting at scale.
 </li>
 <li>
 Retail domain experience strongly preferred, including familiarity with demand vs net sales, sell-through, markdowns, planning vs buying workflows, seasons, price groups, and store vs e-commerce dynamics.
 </li>
 <li>
 Experience working with messy, real-world data and designing models that support business judgment.
 </li>
 <li>
 Ability to reason about semantic or ontology-based data models.
 </li>
 <li>
 Strong communication and relationship skills, comfortable working directly with planners, buyers, and business stakeholders, and able to translate model outputs into decisions, not just insights.
 </li>
 <li>
 Long-term ownership mindset for maintaining and evolving models over time.
 </li>
 <li>
 Hands-on experience building or contributing to Palantir Foundry Workshop applications.
 </li>
 <li>
 Background in wholesale or multi-channel retail, including familiarity with the operational differences between DTC, wholesale, and brick-and-mortar planning cycles.
 </li>
 <li>
 Exposure to agent-based or AI-assisted analytics workflows.
 </li>
 <li>
 Experience working within a formal data governance or steerco-driven prioritization structure.
 </li>
 <li>
 Familiarity with Snowflake-native development patterns including dbt, Snowpark, or similar.
 </li>
 </ul>
 
 
 
 
 
 ADDITIONAL INFORMATION:
 
 
 
 This role sits within the IT organization, reporting to the VP of Information Technology, and operates across the full business including Planning, Merchandising/Buying, DTC, and Supply Chain. Engineering owns ETL, data pipelines, and Snowflake ingestion. BI owns reporting and dashboards. This role owns predictive modeling, decision-support analytics, and model operationalization within Palantir Foundry. Work is prioritized through a cross-functional steering committee with direct business stakeholder representation, ensuring this role has structured visibility into commercial priorities and executive-level exposure from day one.
 
 
 
 
 
 ALICE + OLIVIA is at an inflection point in how it uses data to make decisions. This role is central to that transformation, building the analytics capability that connects our Palantir Foundry investment to real commercial outcomes across every part of the business. This is not a role that maintains existing infrastructure or produces reports. It is a greenfield opportunity to own a strategic function, work directly with the teams making decisions, and build something that compounds in value over time. For the right person, it is a rare combination of technical depth, business proximity, and organizational support.
 
 
 
 
 
 $150,000 -175,000 base salary (This range represents the low and high end of the anticipated base salary range for this NY OR NJ - based position. The base salary will depend on numerous factors such as: experience and qualifications for the role, experience level, skillset, and balancing internal equity relative to other A+O employees.)
 
</div>

Data Scientist

Dicetek LLC

<div>
 
 Summary Of Role
 
 
 
 <ul>
 <li>
 Co-development of analytical engines based on statistical and machine learning algorithms
 </li>
 <li>
 Creation of sustainable digital solutions integrated with existing IT infrastructure in collaboration with business
 
 
 </li>
 </ul>
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Conducts analytical experiments methodically with guidance from technology lead to help solve various problems and make a true impact across multiple domains and situations
 </li>
 <li>
 Process relevant data sources and build clear data-cleaning pipelines to feed data points and variables to the models in a well-structured format
 </li>
 <li>
 Devise and utilize algorithms and models to mine big data stores, perform data and error analysis to improve models, and clean and validate data for uniformity and accuracy for visualization to business
 </li>
 <li>
 Analyze data for trends and patterns, apply statistical and machine learning algorithms, and interpret data with a clear objective in mind in line with project goals and milestones
 </li>
 <li>
 Support the deployment of analytical models into existing IT infrastructure by collaborating with DevOps engineers and Product owners
 </li>
 <li>
 Maintain delivered solutions sustainability after the development phase
 
 
 </li>
 </ul>
 
 ﻿Required Qualifications &amp; Experience
 
 
 
 <ul>
 <li>
 Bachelor's or Master's degree in information systems, data analytics, data science, computer science, economics, risk management or another quantitative related field
 </li>
 <li>
 Experience with Excel, SQL, Python, basic BI-development
 </li>
 <li>
 Proficiency in data processing (Pandas), statistical analysis, and machine learning algorithms
 </li>
 <li>
 Knowledge in building data processing pipelines &amp; ETLs
 </li>
 <li>
 Experience in implementing predictive engines with a focus on time-series forecast
 </li>
 <li>
 Experience in writing API calls based on specifications
 </li>
 <li>
 Experience in automated testing, ability to write mock and unit tests to test both model inputs and outputs
 </li>
 <li>
 Effective collaboration, experience in working with Scrum/Agile teams &amp; task tracking
 
 
 </li>
 </ul>
 
 Additional Considerations
 
 
 
 <ul>
 <li>
 The preferred profile is "all-the-way-rounder" rather than a person with deep expertise in only something specific
 </li>
 </ul>
</div>

Data Scientist (Arabic Speaker)

DICETEK LLC

<div>Summary of role<ul><li>Co-development of analytical engines based on statistical and machine learning algorithms</li><li>Creation of sustainable digital solutions integrated with existing IT infrastructure in collaboration with business</li></ul>
Responsibilities<ul><li>Conducts analytical experiments methodically with guidance from technology lead to help solve various problems and make a true impact across multiple domains and situations</li><li>Process relevant data sources and build clear data-cleaning pipelines to feed data points and variables to the models in a well-structured format</li><li>Devise and utilize algorithms and models to mine big data stores, perform data and error analysis to improve models, and clean and validate data for uniformity and accuracy for visualization to business</li><li>Analyze data for trends and patterns, apply statistical and machine learning algorithms, and interpret data with a clear objective in mind in line with project goals and milestones</li><li>Support the deployment of analytical models into existing IT infrastructure by collaborating with DevOps engineers and Product owners</li><li>Maintain delivered solutions sustainability after the development phase</li></ul> 
﻿Required Qualifications &amp; experience<ul><li>Bachelor's or Master's degree in information systems, data analytics, data science, computer science, economics, risk management or another quantitative related field</li><li>Experience with Excel, SQL, Python, basic BI-development</li><li>Proficiency in data processing (Pandas), statistical analysis, and machine learning algorithms</li><li>Knowledge in building data processing pipelines &amp; ETLs</li><li>Experience in implementing predictive engines with a focus on time-series forecast</li><li>Experience in writing API calls based on specifications</li><li>Experience in automated testing, ability to write mock and unit tests to test both model inputs and outputs</li><li>Effective collaboration, experience in working with Scrum/Agile teams &amp; task tracking</li></ul>
Additional considerations<ul><li>The preferred profile is &quot;all-the-way-rounder&quot; rather than a person with deep expertise in only something specific</li></ul></div>

JPMorganChase

<div>JOB DESCRIPTION 
 
 
Job Summary
The Card Marketing Activation &amp; Transformation team is seeking an experienced &amp; highly motivated Analyst that will focus on the development/maintenance of complexity modeling that integrates into the build of end- to- end forecast modeling across all key teams involved in the execution of a marketing campaign. This is a data intensive position that requires prior background in building predictive models, data modeling solutions, and forecasting techniques.
As a Solutions Analyst, you will leverage statistical and machine learning methods to analyze complex datasets, generate actionable forecasts, and optimize business strategies.
<div></div>Job Responsibilities
<ul><li>Design, build, and validate predictive models to forecast capacity requirements across various teams/roles and predict marketing campaign timelines</li>
<li>Assist in the development of data models and other user tools to support analytics initiatives and ensure data integrity</li>
<li>Apply statistical and machine learning techniques for time series analysis and forecasting.</li>
<li>Collaborate with business stakeholders to understand requirements and translate them into analytical solutions</li>
<li>Communicate model results, insights, and recommendations to technical and non-technical audiences</li>
<li>Monitor model performance and recalibrate as necessary to maintain accuracy and relevance</li>
<li>Document modeling processes, assumptions, and results for transparency and compliance</li>
<li>Stay updated on the latest advancements in predictive analytics, data modeling, and forecasting methodologies</li>
</ul>Required qualifications, capabilities, and skills
<ul><li>Bachelor's degree in a quantitative field or 3 years operational/segmentation analytics or other quantitative experience</li>
<li>4+ years of experience in engineering, statistics, mathematics, or another quantitative field
</li><li>Proficiency in programming languages such as SQL, Python, R, for statistical modelling</li>
<li>Experience with data visualization tools such as Tableau and/or Power BI.</li>
<li>Strong understanding of data modeling concepts, including relational and non-relational databases</li>
<li>Demonstrated ability to query and analyze large datasets to provide meaningful insights.</li>
<li>Ability to understand LLM models and integrate AI capabilities into operational analytics, preferable</li>
<li>Strong analytical and problem-solving abilities</li>
<li>Experience delivering recommendations to leadership</li>
<li>Self-starter with ability to execute quickly and effectively</li>
<li>Strong communication and interpersonal skills with ability to interact with individuals across departments/functions and with senior level executives</li>
</ul>Applicants must be authorized to work for any employer in the US. We are not able to provide immigration sponsorship or take over sponsorship of an employment visa at this time. Final job grade level and corporate title will be determined at time of offer and may differ from this posting.
ABOUT US 
 
 
Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Equal Opportunity Employer/Disability/Veterans 
 
 
ABOUT THE TEAM 
 
 
<div>Our Consumer &amp; Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions &ndash; all while ranking first in customer satisfaction. 
</div><div></div> 
<div>We offer a broad array of credit cards to meet the needs of individuals and small businesses, including Chase-branded and co-branded cards in partnership with well-known companies and organizations. Merchant Services is a leading provider of payment, fraud and data security for companies, capable of authorizing transactions across global currencies.</div></div>

Solutions Analyst - Operations Predictive Modeling & Forecasting

Data Idols

<div><h2 class="jobSectionHeader">Senior Data Scientist</h2>
<h3 class="jobSectionHeader">Salary: &pound;85K &ndash; &pound;95K</h3>
<h3 class="jobSectionHeader">Location: London (Hybrid &ndash; 3 days per week)</h3>

Data Idols are working with an ambitious early-stage technology company that is building a machine learning platform capable of solving complex behavioural prediction problems at scale.

Already working with paying customers and backed by experienced founders with a successful track record of building and exiting technology businesses, they&rsquo;re now looking for a Senior Data Scientist to own one of the most important parts of the company.
<h3 class="jobSectionHeader">
The Opportunity</h3>

This isn&rsquo;t a role where you&rsquo;ll optimise an existing model or become another specialist in a large data science team.

You&rsquo;ll be responsible for designing and building the machine learning capability at the heart of the business. Rather than creating a single predictive model, you&rsquo;ll develop automated systems capable of generating, deploying and continuously improving bespoke machine learning models for every customer.

You&rsquo;ll work with large-scale behavioural event data, applying machine learning, feature engineering and experimentation to improve prediction accuracy and drive measurable customer outcomes.

Working alongside highly experienced founders and recognised experts in machine learning, you&rsquo;ll have the opportunity to influence product strategy, shape technical direction and establish best practices from day one.

This is a genuine ownership role where your work will have a direct impact on both the product and the future success of the business.
<h3 class="jobSectionHeader">
Skills &amp; Experience</h3>
<ul><li>Strong commercial experience building machine learning models in Python</li>
<li>Excellent understanding of predictive modelling, classification or propensity modelling</li>
<li>Experience with feature engineering, model evaluation and experimentation</li>
<li>Comfortable working across the full machine learning lifecycle</li>
<li>Experience working with cloud platforms and modern ML tooling</li>
<li>Curious and analytical mindset</li>
</ul>
If you&rsquo;re looking for a role where you&rsquo;ll solve genuinely difficult machine learning challenges, build something from the ground up and see the direct impact of your work, we&rsquo;d love to hear from you.
<h2 class="jobSectionHeader">
Senior Data Scientist</h2></div><div></div>

Senior Data Scientist

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530 predictive modeler jobs Near Me

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