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Bachelors degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field and minimum 8 years of related experience, of which at least 4 years of directly related data analytics, data science, predictive modeling, machine learning, statistical modeling experience OR advanced in the required fields and 4 years directly related data science, analytics experience.
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6) Perform data acquisition using JSon, SQL, ODBC, JScript, or API for big data extracts and use programming languages like R, Python, SQL,.net, Java or C. Strong knowledge and experience in Data Architecture and Data Modeling, Analytics, Machine Learning (ML) and Artificial Intelligence (AI), Cloud computing, technology roadmap and implementation, Technology advising, strategy and leadership.
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You must be passionate about data management with a solid background in AWS, Databricks, Python, Trino/Starburst, Databases and SQL.Design, develop, monitor, and maintain data pipelines in an AWS Gov Cloud ecosystem with AWS, Databricks, Delta Lake and Trino as the underlying platforms.
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Expertise in Python, R, SQL, statistics, data mining. In this role, you will conduct sophisticated data analytics, data mining, exploratory analysis, predictive analysis, and statistical analysis.
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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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Acting as a Marketing Automation technical domain expert with functional and business teams for technologies like AWS EMR, Snowflake, Python/PySpark, Informatica, Marketo and Knak. Leading on and offshore development teams to develop solutions for different analytical and operational needs for Marketing Automation functions.
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Basic Qualifications Bachelor's degree in Financial Engineering / Quantitative Finance, Math, Statistics, Data Science, Engineering, Economics, or other relevant program. Preferred Qualifications Advanced Degree or CFA (Chartered Financial Analyst Certification.
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Build high-performing data processing frameworks leveraging Google Cloud Platform and utilizing GCP Services like Big Query, Python, Composer, Dataflow, GKE, Pub-Sub, Cloud Monitoring. As a member of the Data and Analytics organization, you will be responsible for designing, building, and maintaining best-in-class data engineering pipelines aimed at driving best-in-class solutions primarily using Cloud platforms like GCP. You will collaborate with cross-functional teams, including data scientists, analysts, business SME’s and software engineers, to ensure the efficient and reliable processing, storage, and retrieval of data.
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Bachelor's degree in a relevant field (computer science, data engineering, etc.) You'll handle administration, configuration, and optimization to support data analytics, machine learning, and data engineering activities.
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You will utilize your knowledge of geospatial data and analytics while using tools such as Geographic Information Systems (GIS), Python, and visualization/analytical platforms (ex.
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Experience working with Python, R, SQL, and Spark for Big Data Analytics. Lead the Analytics Center for Enablement partnering with data scientists, data engineers and product managers to deliver BI, ML and NLP solutions.
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What you will be responsible forDevelop/ Build and enhance data pipelines using Python, Spark based data engineering solutions (Databricks), and SQL (AWS Redshift). Hands on development in Python, PL/SQL, SQL, Shell Scripting, AutoSys. Hands on experience working in cloud data platforms such as AWS Redshift, Spark based data engineering solutions.
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Build near real time data pipeline with Apache Spark, Pyspark & Python to extract data from NoSQL DBs and Load it into Cassandra Data Enterprise Graph. Build real time data pipeline with Apache Flink, Apache Kafka and Python to extract data from Relational and NoSQL DBs and Load it into Cassandra Data Enterprise Graph.
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Design and build data solutions using Databricks, SQL, Python, Spark, and Delta Lake in the Azure ecosystem (Blob Storage, Data Factory, Event Hubs). Lead and contribute to the creation of a self-service data platform for reporting and analytics.
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Data engineering, database engineering, business intelligence, or business analytics, Master’s degree in Computer Science or related field and 2 years' experience in software engineering or related field, We value candidates with a background in creating inclusive digital experiences, demonstrating knowledge in implementing Web Content Accessibility Guidelines (WCAG) 2.2 AA standards, assistive technologies, and integrating digital accessibility seamlessly.
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bachelors degree python data jobs Title: data analyst analytics specialist
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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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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.