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Technical Analyst – Data Science Jobs

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Project Leader – Data Analytics
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Vietnam , Ho Chi Minh City
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TC Data
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Data Scientist
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Mexico
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Tech Holding
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Data Scientist
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United States
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Tech Holding
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Operations Business Analyst 4
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United States , Tualatin
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Lam Research
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Data Analyst Co-op
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Canada , Ottawa
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25.50 - 32.50 CAD / Hour
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Ericsson
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Graduate Analyst - Complete Vehicle Analysis
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United States , Chattanooga
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Volkswagen AG
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Principal Data Analyst
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United States , Plano
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119400.00 - 136200.00 USD / Year
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Capital One
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Senior Data Scientist
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France , Aix-en-Provence
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Voyage Privé
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Data Analyst - Quality Assurance
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India , Bangalore
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Glean
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Senior Technical Pricing Analyst
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United Kingdom , London
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The Emerald Group Ltd, Search and Selection
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Business Analyst
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United States , Iselin
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Robert Half
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Software Engineer II
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United States , Portland
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Solomon Page
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Software Engineer II
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United States , Portland
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Solomon Page
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Data Analyst
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United States , Cedar Park
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Wise Equation Solutions
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Data Analyst
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United States , Houston
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Robert Half
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Python/Pyspark Engineer
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Slovakia , Bratislava
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Signify Technology
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Data Architect
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United States
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InData Labs
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Model Validation Senior Analyst
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India , Mumbai
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Citi
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Junior Data Governance Consultant
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Netherlands , Nieuwegein
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3000.00 - 6000.00 EUR / Month
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Sopra Steria
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Etl Talend Developer
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India , Chennai
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Citi
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Explore Technical Analyst – Data Science jobs and discover a dynamic career at the intersection of data, technology, and business strategy. This hybrid role is designed for professionals who excel at translating complex business challenges into actionable, data-driven solutions. Technical Analysts in Data Science act as the crucial bridge between data science teams and business stakeholders, ensuring that advanced analytical models and insights are effectively integrated into operational processes and decision-making frameworks. Professionals in these jobs typically undertake a wide range of responsibilities centered on enabling data science initiatives. A core duty involves analyzing business processes and collaborating with stakeholders to define problems, which are then translated into detailed technical specifications and requirements. They are often responsible for building and maintaining robust data pipelines and infrastructure to ensure a reliable flow of clean, high-quality data for modeling and analysis. This includes designing and developing scripts, APIs, and automation tools to streamline analytics workflows. Furthermore, they play a key role in developing reports, dashboards, and visualizations that clearly communicate complex insights and key performance metrics to non-technical audiences, thereby democratizing data across the organization. To succeed in Technical Analyst – Data Science jobs, a specific blend of technical prowess and soft skills is essential. On the technical side, proficiency in programming languages like Python and SQL is fundamental, along with experience working with databases (SQL and NoSQL) and cloud platforms such as AWS or Azure. Knowledge of data modeling, ETL processes, and version control systems like Git is standard. Crucially, a strong understanding of the data science toolkit—including libraries like Pandas, Scikit-Learn, and PyTorch for machine learning—is required to effectively support data scientists. Familiarity with emerging fields like Generative AI and LLMs is increasingly valuable. Beyond technical skills, exceptional analytical and problem-solving abilities are paramount. These roles demand excellent communication and stakeholder management skills to gather requirements, explain technical constraints, and present findings. A typical background includes a degree in Computer Science, Data Science, or a related quantitative field, coupled with several years of experience in a technical analysis or similar hybrid role, often within Agile development environments. For those seeking a career that combines deep technical analysis with strategic business impact, Technical Analyst – Data Science jobs offer a challenging and rewarding path. This profession is ideal for individuals passionate about leveraging data to solve real-world problems and drive organizational innovation through scalable data solutions.

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