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Required skills: credit/financial modelling; cloud computing environments; machine learning techniques/algorithms (classification, regression, clustering, feature engineering, decision trees, gradient boosting); statistical skills (distributions, statistical testing, regression, regulation); R; Python; Anaconda; TensorFlow; Scikit-learn; Keras; SQL; H2O.ai; Redshift.
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We seek machine learning engineers with a passion for sports to implement, automate, and optimize the quantitative models that power our world-class sports intelligence platforms in baseball, basketball, cricket, football (American), hockey, soccer, and tennis.
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Machine learning : Strong mathematical foundation and understanding of the concepts underlying machine learning, deep learning, NLP, statistical modeling, and data analysis.
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Strong knowledge in: Regression, Classification, Machine Vision, Natural Language Processing, Deep Learning and Statistical modeling. Travelers Data Scientists are taking the organization to the next level of intelligence by using advanced statistical techniques and machine learning algorithms.
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LICENSING/CERTIFICATION Any certification specializing in data engineering, big data, data science, machine learning, artificial intelligence, or statistical analysis from accredited institutions of higher learning.
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As a Data Scientist you will utilize experience in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least one high level language (e.g. Python), statistical analysis, variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering.
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We are looking for a Senior Data Scientist, to drive advertiser media strategy and performance through advanced statistical modeling and machine learning techniques. Data science, machine learning, optimization models, Master's degree in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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Sound knowledge of complex machine learning algorithms such as regressions, classification, clustering, optimization algorithms, neural nets, decision trees. Programs range from high school completion to bachelor's degrees, including English Language Learning and short-form certificates.
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Advanced knowledge of math, statistics, multivariate analysis, statistical tests Advanced Python coding skills Experience with one or more machine learning frameworks such as TensorFlow and/or PyTorch Understanding of numerical implementations Understanding of cloud computing Knowledge of data querying knowledge of EMG, EEG, ECG, IMU, CV algorithms Experience with C.
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Support National Security clients as a Data Scientist performing data science, analytics, data wrangling, statistical analysis, and machine learning. 1+ years of relevant professional experience in data science / analytics (e.g., data wrangling, data visualization, statistical analysis, predictive analysis, and machine learning.
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Possess direct experience and stay at the forefront of latest trends in machine learning, statistical test design, media mix modeling, artificial intelligence, and other data science fields.
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5+ years of experience performing data science and analytics, such as: data wrangling, data visualization, statistical analysis, predictive analysis, and machine learning. Proficiency using Python for data science: data wrangling, statistical analysis, machine learning models.
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5+ years of experience as a Machine Learning engineer building production-level pre/post-processing data pipelines for ML/DL modelExperience in statistical analysis & visualization on datasets using SQL, Pandas or R.
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Recent or soon-to-be completed Ph. D. (typically completed within 3 years) in Computer Science or Mathematics with strong background in one or more of the following: Statistical machine learning, Bayesian deep learning, probabilistic and differentiable programming, probability and measure theory.
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Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Supervisory experience, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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FEATURED BLOG POSTS
The Effects of Workplace Racism and Sexism
One day it's a covert statement to a mother returning to work after maternity leave. Another day it's a lingering gaze at an employee enjoying a culturally rich meal. These microaggressions (or sometimes macroaggressions) can take an employee from a confident, high-performer to one that feels insecure being themselves at work. Your employees engage with people with different ideas and feel most comfortable and valued when they can work without losing their cultural, racial, and gender identity. While most employers know this, why have workplace racism and sexism often been neglected?
When Rage Applying Strikes: How to Identify Unserious Candidates
As the job market remains highly competitive, we have seen a surge in "rage applying." This is when candidates apply to multiple jobs, often without considering whether they are truly interested in the role. Rage applying goes hand-in-hand with quiet quitting. Often, employees want to entertain the thoughts and feelings of leaving their job, but they aren't necessarily serious about leaving yet. Meanwhile, other employees engaging in this trend are actually trying to find a better role. As a recruiter, it can be hard to identify who are the real applicants in a sea full of quiet quitters, but understanding rage applying and identifying red flags will certainly help.
How to Increase Job Ad Exposure
In today's competitive job market, writing quality job ads is critical for attracting top talent to your organization. While networking and candidate referrals are prime real estate for finding qualified candidates, nothing beats the tried-and-true method of writing an extraordinary job ad. But while writing a great job ad is the first step, what's more important is increasing visibility. You could have the most detailed, well-written ad on the internet, but if no one sees it, then you are wasting time (and potentially money!). Employers often believe that job boards are the root of the problem, but you can learn how to increase job ad exposure by tweaking a few steps of your recruitment process.
How to Navigate Hiring Out of State
The job market has shifted significantly in recent years. The accelerated adoption of technology has not only pushed many companies into remote working arrangements but also increased the availability of supporting tools and technologies (i.e., video conferencing and collaboration software).
Building a Candidate Pipeline Through Internships
Building a candidate pipeline through a great internship program for local college students and recent graduates at local universities is a great and cost-effective way to attract and retain top talent. By offering meaningful and impactful work experiences, regular feedback, coaching, and mentorship, you can create a positive internship experience that will make your organization a sought-after destination for future employees. This not only benefits the organization in the short-term but also in the long-term, as you'll have a pool of well-trained and experienced candidates who may be interested in full-time employment once they graduate. Furthermore, building relationships with local universities and college students can increase brand awareness and build a positive reputation for your organization in the local community.
Hiring Transparency
Transparency in hiring refers to the open and honest communication and information sharing that takes place between employers and job candidates. It encompasses all aspects of the hiring process, from posting job descriptions to providing feedback on performance during and after the interview process. In today's job market, hiring transparency has become increasingly important for both employers and candidates alike.
Recruitment strategies that are weird, but actually work
In the current candidate-driven job market, recruiters are looking for unique ways to attract talent. Some have resorted to even (dare we say it?) recruitment strategies on the border of weird and wacky. What can we learn from the unusual recruitment tactics that are being used and actually getting results? Here’s a rundown of some unique recruitment strategies that actually work.