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Experience in Big Data : 3+ years of experience working in the Big Data ecosystem, particularly with tools like Databricks, Apache Spark, Hadoop, etc. Document Big Data Architectures : Create comprehensive technical documentation for Big Data platforms, including Databricks, Apache Spark, and data lake architectures.
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Cloud & Big Data Integration: Lead the migration and integration of data systems to cloud environments (AWS, Azure, GCP) and implement big data solutions as needed for advanced analytics and reporting.
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Advanced technical certifications: AWS Certified Cloud Practitioner, Solutions Architect, Certified Developer, or SysOps Administrator certifications; RHEL RHCSA/RHCE; AWS Certified Data Analytics, DASCA Big Data Engineering and Analytics preferred.
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Experience with Big Data frameworks (e.g. Hadoop MapReduce, Spark, Hive, Kafka) Expertise with Google Cloud Platform’s distributed Machine Learning, Data Science and Data Engineering tools (e.g. BigQuery ML, VertexAI, AutoML, Docker, Kubernetes, Kubeflow, Dataproc.
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Experience of implementing Amazon EMR or Big Data technologies like Hadoop, Spark, Presto, Hive will be a plus Prior domain experience of Life Insurance, Annuity or Financial Services is a plus AWS Certifications are considered a strong plus Excellent verbal and written communication skills.
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Apache Hadoop or Apache Spark (for big data processing) Data Warehousing (e.g., AWS Redshift, Google BigQuery) Natural Language Processing (NLP) Tools and Frameworks (e.g., Hugging Face, AWS Comprehend Medical for extracting insights from clinical text data.
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Docker, Kubernetes, ECS, EKS or AWS Fargate (for containerization and orchestration of data applications and reproducibility) Data Visualization Tools (e.g., Tableau, Power BI, Plotly) The Data Scientist will be responsible for driving insights from the vast amounts of patient and environmental data available within our data warehouse.
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HIPAA Compliance (handling sensitive patient data securely) Data Privacy (understanding of privacy laws such as HIPAA, GDPR) Work closely with researcher teams to design analysis specifications, including input data specifications, data cleaning, algorithms, and interpretation of results.
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Data Scientist (Healthcare) 100% Remote. Data Science & Machine Learning Frameworks. Python (for preprocessing, data analysis, machine learning, scripting) Data Governance & Security.
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Develop and implement algorithms on existing data warehouse records and identify new external data sources to be ingested to the data warehouse to strengthen analyses. Research and implement AI algorithms, apply off-the-shelf AI and data-centric tools, and collect, store, and maintain data.
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Auditing & Compliance Tools (for ensuring secure and compliant data handling) SAS (common in healthcare data analysis) Data Anonymization or De-identification techniques (for research and compliance.
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XGBoost or LightGBM (for gradient boosting in structured data) Data Tools & Platforms. Clinical Terminologies (e.g., SNOMED, LOINC) Natural Language Processing (NLP) (for analyzing clinical notes or electronic health records.
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TensorFlow, PyTorch, Keras (for deep learning and complex machine learning, including neural networks and advanced AI) ETL Tools (e.g., Informatica, Talend, AWS Glue) AWS SageMaker (for end-to-end machine learning development, training and scalable machine learning in a managed cloud enviornment.
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Bachelors Degree computer science, artificial intelligence, informatics or closely related field. AWS Bedrock (for accessing pre-trained LLMs and foundation models without managing infrastructure) Experience with Epic or Cerner (popular EHR systems in healthcare.
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AWS, Google Cloud, or Azure (cloud platforms for scalable computing) SQL-based Databases (e.g., PostgreSQL, MySQL, Microsoft SQL Server) ICD-10 Coding (for medical diagnosis and procedure classification.
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big data jobs Company: Servicenow in AK, Germany
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Once you’ve finally mustered up the courage to find another job, leaning into the job hunt can feel scary and overwhelming. So, it does nothing to help your confidence when your outbox is full of unanswered follow-ups and interview requests. You thought that ghosting only happens on dating apps—so why do recruiters ghost, too? Recruiter ghosting is not an effective recruiting strategy, but sometimes it’s inevitable. How can you overcome such an unfortunate new career trend? Read on.
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Making the Move to Salary Transparency
The salary transparency trend continues. Last year, Colorado passed its Equal Pay Transparency Rules, which required employers to include compensation in job postings, notify employees about promotional opportunities, and record job descriptions and wage records. Soon after, states like Washington, Nevada, Maryland, and Rhode Island followed suit.
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Recruiting in a Tight Market
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