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Lead Data Scientists typically leverage and implement pre-defined analytics patterns and infrastructure on projects that are moderately complex, requiring working knowledge of a more limited set of machine learning or advanced modeling approaches.
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Experience with data modeling, data prep and machine learning tools like Alteryx, RapidMinder, RStudio and Tableau Prep. Experience with statistical and mathematical modeling, artificial intelligence and machine learning software and methods.
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Their passion for learning is infectious and excites others. In depth knowledge of modern data technologies including Snowflake, RedShift, Azure SQL/Synaspe, Databricks or similar technologies.
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Proving specialization background in data science, including experience with statistical analysis, data mining, predictive modeling, and machine learning; Demonstrating a thorough level of knowledge in statistical modeling, algorithms, data mining, and machine learning algorithms problem solving.
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To excel in this role, you should possess business acumen, hands-on experience in data modeling, proficiency in database manipulation and query languages like SQL, familiarity with tools like Alteryx and data science toolkits such as R or Python, strong project management skills, and expertise in data visualization tools like PowerBI.
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Demonstrating thorough knowledge about ETL tools and techniques, such as tools like Alteryx, Power Query, Azure Data Factory, and Power Apps suite of tools; Demonstrating intimate knowledge about designing and implementing data-driven analyses to monitor for compliance with regulations and standards, such as anti-corruption laws, data privacy regulations, and industry-specific regulations.
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Extensive experience or knowledge of data visualization technologies such as Tableau, Power BI, Qlik and Spotfire. Extensive experience or knowledge of cloud-based data platform technologies such as AWS, Microsoft Azure or Google Cloud Computing.
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Work with business owners and other analysts (with a broad range of analytical expertise) to deliver data products and provide business insights through quantitative analysis (e.g., Predictive Modeling, Machine Learning, Geospatial Modeling, Operations Research, etc.
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Strong knowledge, experience, and fluency in a wide variety of tools including Python with data science and machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch), Spark, SQL; familiarity with Alteryx and Tableau preferred.
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Technical understanding of machine learning algorithms; experience with deriving insights by performing data science techniques including classification models, clustering analysis, time-series modeling, NLP; technical knowledge of optimization is a plus.
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Advanced Python, SQL, and Machine Learning experience. You are responsible for directly sourcing data using languages and tools such as SQL, Python, Alteryx, Excel, and Salesforce, and analyzing that data in ways that support practical decision-making and strategy setting.
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Conduct data management maturity assessments and identify pain points for including data quality, governance, architecture, analytics, metadata management, master data management. Mature, humble, and genuine, SEI-ers frequently go above and beyond for both their clients and their colleagues.
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Develop, train, and deploy statistical and machine learning models for revenue, campaign optimization, customer LTV, click propensity, action sequencing, personalization, and segmentation.
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SEI-ers enjoy working with genuine, thoughtful folks who want to steer clear of the traditional grind and share the joy of day-to-day life and activities with colleagues, friends, and family. Must have experience in architecting and implementing data architecture, data engineering, reporting and analytical solutions across multiple business functions and domains.
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Experience deploying enterprise-wide reporting solutions while leveraging data visualization best practices. Data Strategy and Governance. Should also be able to Identify the implications for data governance that arise from the technology, current processes, and skill levels in a complex data environment.
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