Sr Advanced Data Scientist

Honeywell
1 month ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior
Compensation
£ 64K

Job location

Tech stack

Artificial Intelligence
Data analysis
Artificial Neural Networks
Distributed Data Store
Hadoop
Hive
Statistical Hypothesis Testing
Python
Machine Learning
Natural Language Processing
NoSQL
SQL Databases
Data Streaming
Support Vector Machine
Tableau
Feature Engineering
PyTorch
Spark
Deep Learning
Naive Bayes
Keras
Information Technology
Spark Streaming
K Means
Microservices

Job description

As a senior advanced data scientist, you will join a high-performing, global team, and be responsible for designing, developing, and implementing data driven solutions for all Honeywell business groups and functions. You will work closely with application architects to integrate results into operational platforms, including Hadoop and NoSQL architectures.

You will also be responsible for recommending innovative solutions by using various data science methods including hypothesis testing, feature engineering, and also be responsible for defining the data acquisition strategy when required.

This includes stakeholder management by presenting regular updates and final results to senior leadership of the customer organization.

You will also be expected to actively participate in defining and governing our analytics strategy for Honeywell building out AI/ML capabilities of our Forge platform and promoting data science methods and processes across functions.

You will report to the Data Science Site Leader in the Honeywell Industrial Analytics organization, part of the Connected Enterprise.

Requirements

We're looking for a new team member for our Global Finance analytics team who is motivated by cracking tough challenges with data, trained in problem solving, and with an unending thirst for learning., Master's degree in Computer Science, Engineering, Applied Mathematics or related field

Exposure to Finance domain and use cases in larger global enterprise setting

Minimum of 3-5 years of Data Science prototyping experience (Python and/or R tool-stack) using machine learning techniques and algorithms such as as k-means, k-NN, Naïve Bayes, SVM, Decision Trees

Minimum of 3-5 years of Machine Learning experience of physical systems

Minimum of 2-4 years of experience with distributed storage and compute tools (e.g. Hive and Spark)

Minimum of 1-2 years of experience in deep learning frameworks like PyTorch, Keras

Experience with designing, building models and deploying pipelines to production using containerized microservices and/or orchestrated batch runs

We Value

PhD degree in Computer Science, Engineering, Applied Mathematics or related field

Experience with Natural Language Processing models

Experience with Streaming Analytics (i.e. Spark Streaming)

Experience with Recurrent Neural Network architectures

Experience with Image Analytics

Experience with SQL

Experience with Tableau

Experience working with remote and global teams

Results driven with a positive can-do attitude

YOU MUST HAVE

  • Bachelor's degree from an accredited institution in a technical discipline such as the sciences, technology, engineering or mathematics

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