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Armstrong Fluid Technology

Compare

3.7

based on 43 Reviews

42 Armstrong Fluid Technology Jobs

Senior Data Scientist - Program Developer

Armstrong Fluid Technology

3.7

based on 43 Reviews

5-10 years

Bangalore / Bengaluru

1 vacancy

Senior Data Scientist - Program Developer

Armstrong Fluid Technology

posted 6mon ago

Job Role Insights

Flexible timing

Key skills for the job

Medical Coding Data Analysis Data Analytics Python Logistics Web Development

+ 4 more

Job Description

The Senior Data Scientist Program Developer leverages a combination of techniques from statistics, machine learning, computer science, optimization and data visualization to produce
advanced predictive analytical solutions that determine patterns and relationships in large volumes of data to diagnose past, and predict future behaviors, events and outcomes.

The successful candidate will be the most senior ML/AI SME on a larger company wide team overseeing Armstrong s larger digitalization transformation. Working in a cross-functional role with Engineering, IT professionals, and business domain experts, taking on a variety of tasks including but not limited to mining data, building models, building dashboards, and conducting detailed analysis. The ideal candidate will have an analytical mindset, deep understanding of databases, visualization, and modeling techniques, and the ability to thrive in a globally diverse and dynamic environment delivering against tight deadlines. This position is located in Bangalore India, with telecommuting allowances where needed. In addition to annual initiatives, the key accountabilities of this position are as follows:

General

Serve as the most senior subject matter expert in Data Science, Data Engineering. and related processes for the entire global Armstrong Organization
Own the entire Data Science program and development lifecycle from ideation to opportunity sizing, measurement design to experimentation, and from causal analysis to post-deployment learning, and iteration into the next development cycle.
Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
Develop and maintain advanced knowledge of Armstrong s business and IT methodologies and apply this knowledge to initiative work.
Develop presentations and documentation to describe project work to management, stakeholders, and non-technical people within the organization.
Deliver, track, and articulate developed deployed ML/DL/AI applications to internal senior and executive audiences.

Technical Development

Collect data from disparate structured, semi-structured, and unstructured data sources in support of model development.
Extract data using data mining techniques to uncover trends and derive insights; analyze forecasting metrics.
Perform exploratory data analysis with the goal to uncover trends, find root causes of forecast inaccuracies and develop innovative features that can drive additional value
Analyze data from different sources and provide key insights throughout the strategy, design, development and deployment stages.
Work with production and core engineering technical staff to build and maintain predictive machine learning models (predictive maintenance, energy management, regression-based metering, and health and safety) uniquely crafted for Armstrong s family of products and services
Work closely with software engineering teams to drive real-time model implementations and new feature creations
Leverage existing data sources, identify and mine data from new sources, identify opportunities for and execute primary data gathering efforts, and provide recommendations on scaling new methods more broadly
Provide coding integrations for data flows between existing and new systems. This will include gathering data from local and web-based sources while working with IT professionals to design streaming data pipelines.
Perform model tuning and frequent training to maintain the proper health of predictive models by ingesting newer data sources.
Monitor the model performance and make ongoing corrections by executing extensive model testing strategies.

Program Development

Build predictive models leveraging advanced ML/DL techniques, tools and analytics frameworks, deploy in production environments, and expose actionable insights to support Armstrong s Active Performance Management program.
Develop initiatives to assist Production, Engineering, Logistics, and Mechanical teams in a number of highly technical areas
Collaborate to implement data best practices with Armstrong s global IT, OT, and Data Lake teams.
Identify unexpected problems in the application of complex data analysis tools and develop solutions.
Demonstrate enthusiasm and discipline while being able to take ownership, make timely decisions and communicate outcomes.
Understand and satiate the business requirements and processes around advanced analytics model execution.
Build machine learning models with advanced analytics techniques to extend Armstrong s capabilities and deliver new business value.

Role Requirements

Master s degree in one or more technology/analytical field such as Computer Science, Management of Information Systems, Mathematics, Statistics, Engineering, Machine Learning/AI, or other relevant technical fields is a must.
PhD in Statistics, Mathematics, Quantitative Methods, or equivalent is preferred.
3-5 years work experience in Data Science (statistical data analysis, data transformation, regression and classification machine learning modeling, or data engineering) executing the development & deployment of production code for Machine Learning models and applications, as well as managing the iterative Predictive Modeling Lifecycle
5-10 years work experience in Agile software & web-development is a plus.
Deep expertise in developing and maintaining predictive models using advanced ML/DL techniques. Proficiency in building Supervised learning models, Generalized Linear Models, Discriminant models, and Unsupervised learning models.
Excellent programming knowledge in one or more of the common Data Science scripted languages (SAS, Python, R, Spark/Scala).
A strong programming background in Python with a focus on data science skills including Jupyter Notebooks and common data science libraries is a strong plus. Facility with computational algorithms is a strong plus.
Experience in building Deep neural networks and the use of AI/Deep Learning frameworks like Caffe 2, Tensorflow, Theano, Keras is a strong plus
Expertise in data extraction, manipulation, feature engineering and validations.
Experience in working with any of the industry-standard analytical/ML modern ML platforms like IBM Watson Studio, Cloudera, Amazon Sage Maker, Azure ML.
Previous experience with visual business intelligence and analytics software such as Tableau, Spotfire or Power BI considered an asset.
Experience in managing and leading complex projects and multiple projects simultaneously, including (but not limited to) building descriptive, predictive and prescriptive solutions, and deploying said solutions in the cloud and/or on-premise production environments.
Demonstratable experience translating data science investigations into relatable business solutions.
Experience/exposure to the Manufacturing &/or HVAC industry considered an asset but not required.
Self-starter & self-directed with the ability to work autonomously, as well as collaboratively, with a focus on results
Strong organizational skills and the ability to manage multiple tasks simultaneously.
Creative problem-solving skills, conflict management within different organizations (centralized and matrix organizational structures)
Ability to break down complex problems in a simplified way, conduct root cause analysis and provide clear, well thought-out recommendations.
Strong, team-oriented leadership skills with presence and a bias for action.
Ability to communicate in an open and authentic manner in all situations, with exceptional interpersonal, persuasion, and communication skills to communicate strategic and technical ideas to internal audiences, and both inform and solicit buy-in from the end-user community.