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BS, MS, or PhD degree in Computer Science, Data Science, Economics, Statistics, Computational Biology, BioInformatics, or equivalent experience. You have a strong background in data science, graph mining, and graph simulations.
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Advanced proficiency with data, analytics, and visualization technologies (we use SQL, Looker, and Python). As a Data Scientist on the Square Loans team, you will partner with cross functional stakeholders like Product, Credit Policy, Engineering, and Operations to shape and execute strategies that expand credit eligibility, enhance loan gross profit, and improve cohesive banking user experience on a global scale.
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As a Data Scientist you should be comfortable discussing statistical and machine learning methodology and have significant experience with Machine Learning systems, working with the details of algorithms, not just the tools' application.
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In this role, you’ll be embedded in the Risk & Fraud organization and work closely with machine learning, product management as well as other cross-functional partners to drive strategy using a variety of data science techniques and own north star metric monitoring.
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The Data Scientist will formulate Glo’s data strategy and work with the Data Engineer to implement the appropriate architecture and execute on the strategy. At least 5 years of experience in data science, machine learning, and statistical modeling.
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Proficient in programming languages commonly used in image data analysis, such as Python, R, MATLAB, or ImageJ/FIJI. As a Scientist on our Delivery team, you will perform scientific and data analysis while helping to drive our client projects.
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As a data scientist, you will be tackling time-series based machine learning problems to help build the next generation of algorithms that will provide early prediction for these patients and their doctors.
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Master’s degree in Data Science, Mathematics, Statistics, Biostatistics, Bioinformatics, Biological Sciences, or related discipline. Quantum Leap Healthcare Collaborative (QLHC) is seeking an exceptional Data Scientist with strong analytical and computational skills to support multiple complex clinical trials in Oncology and COVID. The Data Scientist is responsible for performing data analytical and programming activities in these trials, which may be Phase I, II and III pharmaceutical trials and medical device trials evaluating new biomarkers and/or therapeutics.
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3+ years combined as a solution engineer, application engineer, data scientist, senior data analyst or a similar technical role at a fast-growing technology company. Ability to understand large data sets and use appropriate technologies and methodology to manipulate data (e.g. SQL, R, Python, ETL pipelines.
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Prior experience in data science/AI/BI tools. Nifi, Python, kafka, Databricks, Informatica or any other integration technologies. Prior experience building an investments data warehouse covering common data domains (clients, portfolios, funds, holdings, transactions, trades, securities/investments, ESG, analytics, performance, IBOR/ABOR/TBOR, benchmarks/indices) or with vended products that provide similar functions (e.g. Eagle, State Street Alpha platform.
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Leverage your advanced skills in Python and SQL and with platforms like Databricks and AWS to build highly scalable data pipelines and warehouses. Core Tools: Python, R, SQL, Next.js, React, TypeScript, Tailwind CSS.
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We are open to proficiency in a variety of technologies: SQL, Bash, Python, Java, Presto, Spark, AWS, DataOps, DevSecOps, data streaming like Kafka, RabbitMQ, data stacks like Airflow, Databricks, dbt, and data stores like Cassandra, Aurora, ZooKeeper.
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Our primary developer offering is an ultra-performant Data Processing Framework (unified streaming + batch) with a Python API, distributed Rust engine, and capabilities for data source integration & transformation at scale (Kafka, S3, databases/CDC.
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Senior Scientist: experience leading preclinical drug discovery project teams and/or writing IND reports is a plus. Ph. D. in immunology, biomedical sciences, cancer biology, or related field with 0-2 years industry experience (Scientist I), 3-4 years of industry experience (Scientist II), or 5-6 years of industry experience (Senior Scientist.
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Collaborate with cross-functional teams to integrate metabolomics data with other 'omics data, such as genomics and proteomics, to gain insights into disease biology. Analyze and interpret metabolomics data to identify biomarkers and metabolic pathways associated with disease.
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data science scientist python jobs Company: Noralogic Inc in San Francisco, Grove-city, Ohio
FEATURED BLOG POSTS
Recruiting in a Recession: Hard Truths That Talent Acquisition Experts Must Accept
The summer had economists from around the globe embroiled in a debate about a possible recession coming in the next few years (or months). As of October 2022, the U.S. Labor Department data put the current inflation rate at 7.7%. The recent layoffs in the tech industry are just the first of what is soon to be a string of cutbacks by companies looking to save costs. For recruiters, this means freezes in hiring and fewer openings. It will also include the uphill task of finding the best candidates for them from the coming influx of recently laid-off job seekers. Now is probably a good time to brace for tough times in the next few years in the talent acquisition industry. To survive and thrive recruiting in a recession, here are some hard truths you will need to accept.
10 Importancies of Setting Realistic Goals
We’ve all heard how important it is to set professional and personal goals. Developing and establishing goals keeps us motivated and moving forward in life. But not all goals are created equal. If you’re chasing goals that are too lofty, you’ll end up disappointed when you cannot reach them. Setting goals that are achievable and measurable is the key to success.
Email Etiquette Principles - Why is it Important
Why is email etiquette important? Let's imagine you're hiring for a new role, and you’ve just received the email below.
10 Reasons HR is Important to an Organization
"Nothing we do is more important than hiring and developing people."
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The world of work has drastically changed in the past few years. Where a good salary and a nice office might have been enough to attract talent in the past, employees today expect flexibility, growth opportunities, and a healthy work environment. In fact, 77% of applicants say they’d consider a company’s culture before applying for a job.
Collaborative Recruiting: The Key to a Better Talent Acquisition Strategy
Talent acquisition is a multi-stage process where candidates undergo various application steps before getting hired. The unfortunate reality is that it is a labor-intense system, with the hiring manager and recruiter often handling all of the work on their own. Ask any one of them, and you will hear about the overabundance of applications and the demanding task of filtering through them to find the best candidates. The quality of talent suffers under the weight of all that work on one person's hands. It's not easy, but as many companies are starting to realize, there is a better way. The future of talent acquisition lies in collaborative recruiting!
4 Talent Acquisition Trends Going Into 2023
For better or worse, a side effect of the COVID-19 pandemic was a marked shift in talent acquisition practices worldwide. With the struggle to retain talent that began in 2020, companies have had to rethink recruitment strategies. The result has been new talent acquisition trends that are well on their way to becoming commonplace. These are the practices that are going to become even more widespread going into 2023.