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Experience with cloud computing platforms (, AWS, Azure, GCP) and cloud native computingProficiency in programming languages like Python, Java, Scala, C#, SQLExpertise in big data technologies such as Hadoop, Spark, Kafka, and NoSQL databasesKnowledge of data modeling, ETL processes, and data warehousing conceptsExceptional problem-solving abilities and teamwork skills.
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Candidates should demonstrate expert knowledge in one or more of the following areas: data management, machine learning, deep learning, artificial intelligence, data visualization, programming Python, programming in R, databases, and related topics, either through a record of teaching excellence or through industrial experience.
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Deep hands-on experience with core technologies such as Airflow, dbt, git, docker. Tableau, Streamlit, AWS, Snowflake, and Python. Combine both internal and external data to conduct analysis to provide meaningful insights on risk management strategies and the macroeconomic environment.
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You will utilize your deep expertise in Python, SQL, ELT processes, AWS, Redshift, and Snowflake to create and optimize data solutions that adhere to best practices in software engineering.
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Strong experience with machine learning technology, such as: big data stack, Python, R, and visualization techniques. Extensive experience with, both traditional SQL and modern NoSQL data stores including SQL, and large-scale distributed systems such as Hadoop and or working in Snowflake/Databricks.
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With its strong 55-year heritage and deep industry expertise, Capgemini is trusted by its clients to address the entire breadth of their business needs, from strategy and design to operations, fueled by the fast evolving and innovative world of cloud, data, AI, connectivity, software, digital engineering and platforms.
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Experience leveraging complex data to drive business decisions, hands-on experience in data science methodologies (predictive analytics, machine learning, patient level data triggers) using R, Python, Databricks, and deep knowledge of Qlik, PowerBI, Tableau for visualization.
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Partner with senior leaders in the Independent Risk Management function to provide consultancy and translate the business needs to design and develop tools, techniques, metrics, and dashboards for insights and data visualization.
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Adept at using Python packages for data manipulation (pandas, numpy), visualizations (seaborn, plotly), and web applications (streamlit, solara) Understanding of concepts in natural language processing and deep learning.
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Net ¬タᄁ Excellent analytical and problem solving skills ¬タᄁ Deep understanding of Capital Markets with focus on Credit Risk, Market Risk or Capital calculations ¬タᄁ Understanding of regulations within the Capital Markets space (Basel, CRR, CRD IV etc) ¬タᄁ Attention to detail and self-starter, with an ability to identify risks and issues and suggest improvements.
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Python or related language- Experience with neural deep learning methods and machine learningPREFERRED QUALIFICATIONS- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
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In our Data science track we prepare you to get job as one of the following: Python developer, a data analyst, data visualization developer, a statistician, a machine learning engineer or a data scientist.
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Strong knowledge of SQL, Python, data-structured languages, and big data. Hands-on experience building statistical, machine learning, and deep learning models. The ideal candidate will be able to creatively digest and analyze large quantities of trading data, including consolidated audit trail (CAT) data, and build models and tools to detect and investigate potentially problematic trading activity.
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Required Skills: A senior delivery manager with 10+ years of relevant experience in the definition, analysis, implementation of data management related initiatives Financial service industry acumen and experience to lead large cross-functional initiatives in information architecture, reference models management and data integrations Deep understanding of data architecture principles, database technologies (SQL and NoSQL), data integration, BI and ETL processes.
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Proficiency in Python, SQL, and cloud-based data processing tools. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data.
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Virtual Reality Job Interviews
With the advent of desktop computers, the arduous task of scouring through weekly job classifieds became a thing of the past. The mid-1990s brought about a new era where job seekers could easily search and apply for jobs online. The introduction of AOL's Instant Messaging feature provided an even faster means for employers and candidates to communicate and schedule interviews. As smartphones became more pervasive in the early 2000s, hiring managers increasingly used phone calls for screening and interviewing candidates. Despite this trend, over 80% of interviews still took place in person.
A Potential TikTok Ban?!
As you may already know, there has been a lot of talk lately about the possibility of a TikTok ban. While this has not yet come to fruition, it's important to consider the implications this could have for businesses and recruiters who rely on TikTok as a platform to market their brand, recruit new talent, and connect with their audience.
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.
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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).