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Causal Inference Machine Learning Postdoctoral Jobs in New Jersey

Campbell Soup Company

Senior Data Scientist

Camden, NJ · On-site

... and unsupervised learning, forecasting, experimentation, causal inference, and optimization ... Experience developing and deploying machine learning solutions using libraries and frameworks such ...

Campbell's

Senior Data Scientist

Camden, NJ · On-site

... and unsupervised learning, forecasting, experimentation, causal inference, and optimization ... Experience developing and deploying machine learning solutions using libraries and frameworks such ...

The Campbell's Company

Senior Data Scientist

Camden, NJ

$109K - $150K/yr

... and unsupervised learning, forecasting, experimentation, causal inference, and optimization ... Experience developing and deploying machine learning solutions using libraries and frameworks such ...

Walmart

(USA) Principal, Data Scientist

Hoboken, NJ · On-site

Strong experience in machine learning: classification models, regression models, NLP, forecasting, unsupervised models, optimization, graph ML, causal inference, causal ML, statistical learning ...

Walmart

Summer 2027 Corporate Intern: Economist

Kearny, NJ · On-site

$25 - $48/hr

... in learning more * You're collaborative and uplift your team when you partner with others * You enjoy hard problems * You have knowledge of experimentation and causal inference methods Walmart and ...

Walmart

Summer 2027 Corporate Intern: Economist

Newark, NJ · On-site

$25 - $48/hr

... in learning more * You're collaborative and uplift your team when you partner with others * You enjoy hard problems * You have knowledge of experimentation and causal inference methods Walmart and ...

Walmart

Summer 2027 Corporate Intern: Economist

Linden, NJ · On-site

$25 - $48/hr

... in learning more * You're collaborative and uplift your team when you partner with others * You enjoy hard problems * You have knowledge of experimentation and causal inference methods Walmart and ...

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Causal Inference Machine Learning Postdoctoral information

What is a causal inference machine learning postdoctoral researcher?

A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.

What are the key skills and qualifications needed to thrive as a causal inference machine learning postdoctoral researcher?

To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.

What are some common challenges faced by causal inference machine learning postdoctoral researchers when integrating causal models with real-world data?

Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.

What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.

Is it difficult to get a causal inference machine learning postdoctoral position?

Securing a causal inference machine learning postdoctoral position can be competitive due to specialized skills required, such as expertise in statistical methods, programming (e.g., Python or R), and a strong research background. Candidates with relevant publications, strong recommendations, and experience in machine learning frameworks often have better chances, but the availability of such positions varies by institution and funding.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in New Jersey?

For Causal Inference Machine Learning Postdoctoral jobs in New Jersey, the most frequently searched job titles are:

What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in New Jersey look for?

The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in New Jersey are:

What cities in New Jersey are hiring for Causal Inference Machine Learning Postdoctoral jobs?

Cities in New Jersey with the most Causal Inference Machine Learning Postdoctoral job openings:

Causal Inference Machine Learning Postdoctoral jobs near you

Senior Data Scientist

Camden, NJ • On-site

Campbell Soup Company
See more jobs at Campbell Soup Companyexternal

Full-time

Medical, Dental, Life, Retirement, PTO

Posted 29 days ago

Job description

Since 1869, we've connected people through food they love. We're proud to be stewards of amazing brands that people trust. Our portfolio includes the iconic Campbell's brand, as well as Cape Cod, Chunky, Goldfish, Kettle Brand, Lance, Late July, Pacific Foods, Pepperidge Farm, Prego, Pace, Rao's Homemade, Snack Factory, Snyder's of Hanover. Swanson, and V8.
Here, you will make a difference every day. You will be supported to build a rewarding career with opportunities to grow, innovate and inspire. Make history with us.
Why Campbell's...
  • Benefits begin on day one and include medical, dental, short and long-term disability, AD&D, and life insurance (for individual, families, and domestic partners).
  • Employees are eligible for our matching 401(k) plan and can enroll on the first day of employment with immediate vesting.
  • Campbell's offers unlimited sick time along with paid time off and holiday pay.
  • If in WHQ - free access to the fitness center. Access to on-site day care (operated by Bright Horizons) and company store.
  • Giving back to the communities where our employees work and live is very important to Campbell's. Our "Campbell's Cares" program matches employee donations and/or volunteer activity up to $1,500 annually.
  • Campbell's has a variety of Employee Resource Groups (ERGs) to support employees.

HOW YOU WILL MAKE HISTORY HERE...
As a Senior Data Scientist, you will serve as a hands-on technical leader responsible for scoping, developing, and operationalizing advanced statistical, machine learning, and AI solutions that deliver measurable business value for Campbell's. You will partner with business and technical leaders across the organization to transform complex, ambiguous challenges into scalable, production-ready analytics products. Through your expertise, leadership, and mentorship, you will elevate data science capabilities, influence strategic decision-making, and help position Campbell's as a data-driven market leader and preferred retail partner.
WHAT YOU WILL DO...
  • Lead the end-to-end development of advanced analytics solutions, including Machine Learning, Deep Learning, Artificial Intelligence, Optimization, Simulation, Data Mining, and Multivariate Statistical techniques such as clustering, regression, PCA, hypothesis testing, and factor analysis.
  • Translate complex and unstructured data into actionable insights by sourcing, cleansing, engineering, and validating data while ensuring reproducibility, quality assurance, and model governance.
  • Own the deployment, monitoring, and lifecycle management of production models in partnership with Data Engineering and ML Engineering teams, including CI/CD, drift detection, model retraining, and performance monitoring.
  • Partner with stakeholders across Supply Chain, Finance, Marketing, Sales, and R&D to identify high-impact opportunities and develop data science solutions that generate measurable business outcomes and ROI.
  • Design and lead experimentation strategies, including A/B testing and quasi-experimental approaches, to evaluate business initiatives and support data-driven decision-making.
  • Develop compelling visualizations, presentations, and business narratives that translate technical findings into actionable recommendations for senior leadership and non-technical audiences.
  • Collaborate with data engineering teams to enhance data and machine learning platforms, evaluate emerging technologies, and recommend improvements that increase scalability, speed, and reliability.
  • Mentor and coach junior data scientists through code reviews, methodology guidance, technical leadership, and best practices.
  • Contribute to the development of analytics standards, reusable assets, technical documentation, recruiting efforts, training programs, and data science communities of practice across the organization.

WHO YOU WILL WORK WITH...
  • Data Science, Data Engineering, and ML Engineering teams
  • Supply Chain, Finance, Marketing, Sales, and R&D leaders
  • Business stakeholders and decision-makers across Campbell's
  • Internal analytics communities and cross-functional project teams
  • Senior leadership teams responsible for strategic business initiatives
  • External retail partners as part of joint value creation opportunities

WHAT YOU BRING TO THE TABLE... (MUST HAVE)
  • Master's degree in Statistics, Computer Science, Mathematics, Engineering, Operations Research, or a related quantitative field; equivalent practical experience will also be considered.
  • 4+ years of hands-on experience developing and deploying advanced analytics, machine learning, and statistical models in production environments.
  • Strong foundation in data science methodologies, including probability, statistics, supervised and unsupervised learning, forecasting, experimentation, causal inference, and optimization.
  • Advanced proficiency in Python and/or R with experience building scalable, modular, tested, and version-controlled analytical solutions.
  • 2+ years of experience working with SQL and modern data platforms such as Snowflake, Databricks, Spark, or comparable technologies.
  • Experience developing and deploying machine learning solutions using libraries and frameworks such as pandas, NumPy, SciPy, scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, and statsmodels.
  • Experience with MLOps and software engineering best practices, including Git, Docker, MLflow, CI/CD processes, model monitoring, and production support.
  • Ability to design experiments, establish success metrics, quantify business impact, and effectively communicate findings to both technical and non-technical audiences.
  • Strong problem-solving, stakeholder management, and communication skills with the ability to influence decision-making and drive adoption of analytical solutions.
  • Demonstrated experience leading complex projects and mentoring junior team members.

IT WOULD BE GREAT IF YOU HAVE... (NICE TO HAVE)
  • PhD in Statistics, Computer Science, Mathematics, Engineering, Operations Research, or a related quantitative discipline.
  • Experience with optimization modeling and tools such as Gurobi, PuLP, or similar frameworks.
  • Experience with cloud-based analytics and machine learning platforms, particularly Microsoft Azure, including Databricks, Synapse, AKS, Azure Functions, and cloud storage services.
  • Experience building analytical applications, dashboards, or decision-support tools using Power BI, Tableau, Plotly Dash, or similar technologies.
  • Familiarity with feature stores, advanced production MLOps frameworks, and large-scale model governance practices.
  • Experience within Consumer Packaged Goods (CPG), retail, or related industries.
  • Expertise in business domains such as demand forecasting, pricing and promotions, marketing mix modeling, assortment optimization, trade spend analytics, or supply chain optimization.

Experience contributing to recruiting, training, and capability-building initiatives within data science or analytics organizations.
Compensation and Benefits:
The target base salary range for this full-time, salaried position is between
$109,400-$150,400
Individual base pay depends on work location and additional factors such as experience, job-related skills, and relevant education or training. Total pay may include other forms of compensation. In addition, we offer competitive health, dental, 401k and wellness benefits beginning on the first day of employment. Please ask your Talent Acquisition Partner for more information about our total rewards package.
The Company is committed to providing equal opportunity for employees and qualified applicants in all aspects of the employment relationship, including consideration for employment, without regard to race, color, sex, sexual orientation, gender identity, national origin, citizenship, marital status, protected veteran status, disability, age, religion, or any other classification protected by law.

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