Sr. Data Scientist

With more than 135 years of financial experience and over 24,000 partners, we serve the world’s most sophisticated clients using leading technology and exceptional service. Major Duties :• Develop software, typically in Python, to independently acquire data from disparate sources (databases, files, APIs, etc.) and combine them into appropriate training , validation and testing datasets • Analyze raw datasets using descriptive statistics, working directly with domain experts to understand the meaning of data fields • Build unit tests, data quality checks and data pipelines to ensure that algorithms use trusted data • Develop and maintain an understanding of many algorithms across supervised learning, unsupervised learning and time series analysis • Propose and develop machine learning ensemble methods that exhibit the best out-of-sample characteristics possible given the input dataset • Utilize expertise in machine learning algorithms to tune algorithms using available hyper-parameters and carefully select feature subsets • Discover biases or leakage in datasets and ensure that train/test splits reflect realistic expectations of real world performance • Run large scale (either in parallel and/or distributed) training and inference jobs on private or public cloud infrastructure • May present findings to internal and external customers using both data science language (F1 scores, regression error, statistical significance, etc.) as well as business domain specific language gained from experience analyzing the data in scope. • Provide some guidance to other software development teams as Data Science Lab prototypes are engineered for full production environments • Work across multiple projects in a fluid environment where work is required across the full research lifecycle from forming a hypothesis, acquiring data, and developing ETL-style software to presenting findings. • Plan and execute data science training sessions and hackathons • Work with external parties (vendors, universities, etc.) to incorporate new techniques and tools into the data science lab • Solves complex problems • Takes a new perspective on existing solutions • Exercises judgment based on the analysis of multiple sources of information • Impacts a range of customer, operational, project or service activities within own team and other related teams • Works within broad guidelines and policies Knowledge :• Python, Common Python libraries (numpy ,pandas, sklearn, etc.), Linux based operating systems, and basic development tools (Python IDEs, source control, etc.) required • Advanced distributed machine learning frameworks (e.g. Keras, TF, etc.), Azure cloud infrastructure preferred • Requires in-depth conceptual and practical knowledge in own job discipline and basic knowledge of related job disciplines • Applies best practices and how own area integrates with others • Explains difficult or sensitive information; works to build consensus

Major Duties :• Develop software, typically in Python, to independently acquire data from disparate sources (databases, files, APIs, etc.) and combine them into appropriate training , validation and testing datasets • Analyze raw datasets using descriptive statistics, working directly with domain experts to understand the meaning of data fields • Build unit tests, data quality checks and data pipelines to ensure that algorithms use trusted data • Develop and maintain an understanding of many algorithms across supervised learning, unsupervised learning and time series analysis • Propose and develop machine learning ensemble methods that exhibit the best out-of-sample characteristics possible given the input dataset • Utilize expertise in machine learning algorithms to tune algorithms using available hyper-parameters and carefully select feature subsets • Discover biases or leakage in datasets and ensure that train/test splits reflect realistic expectations of real world performance • Run large scale (either in parallel and/or distributed) training and inference jobs on private or public cloud infrastructure • May present findings to internal and external customers using both data science language (F1 scores, regression error, statistical significance, etc.) as well as business domain specific language gained from experience analyzing the data in scope. • Provide some guidance to other software development teams as Data Science Lab prototypes are engineered for full production environments • Work across multiple projects in a fluid environment where work is required across the full research lifecycle from forming a hypothesis, acquiring data, and developing ETL-style software to presenting findings. • Plan and execute data science training sessions and hackathons • Work with external parties (vendors, universities, etc.) to incorporate new techniques and tools into the data science lab • Solves complex problems • Takes a new perspective on existing solutions • Exercises judgment based on the analysis of multiple sources of information • Impacts a range of customer, operational, project or service activities within own team and other related teams • Works within broad guidelines and policies Knowledge :• Python, Common Python libraries (numpy ,pandas, sklearn, etc.), Linux based operating systems, and basic development tools (Python IDEs, source control, etc.) required • Advanced distributed machine learning frameworks (e.g. Keras, TF, etc.), Azure cloud infrastructure preferred • Requires in-depth conceptual and practical knowledge in own job discipline and basic knowledge of related job disciplines • Applies best practices and how own area integrates with others • Explains difficult or sensitive information; works to build consensus Experience :• Computer Science degree (undergraduate or graduate level) and strong statistical background. Data Science specific graduate work, Finance sector experience or coursework preferred • Acts as a resource for colleagues with less experience; • May lead small projects with manageable risks and resource requirements