Job Description:

AVP, Lead Machine Learning Engineer

Data and Analytics function within the global Information Technologies leads the development and operationalization of Artificial Intelligence, Cognitive Computing and Machine Intelligence applications. Currently, we are looking for an experienced AI/ML engineer who is keen to involve in new initiative of deep learning projects across our business areas globally.

The ideal candidate will be passionate about artificial intelligence and machine learning as well as stay up to date with the latest developments in the field. You are a self-starter who will take ownership of your projects and deliver high-quality data-driven machine learning solutions.  You are adept at solving diverse business problems by utilizing a variety of different tools, strategies, algorithms and programming languages.

Responsibilities

Lead Machine Learning Engineer is responsible for understanding the business problems, identifying and applying right artificial intelligence/machine intelligence technologies to solve problems and involving in formulation and execution of technologies recipes for operational deployments.

• Understanding business objectives and developing models that help to achieve them, along with metrics to track their accuracy and performance


• Evaluating ML algorithms that could be used to solve a given problem and ranking them by their success probability


• Collect and analyze data from diverse internal and external source systems and assess the effectiveness and accuracy of the data and data gathering techniques


• Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world


• Verifying data quality, and/or ensuring it via data cleaning


• Developing strategies for curating/labelling data for model training data and implementing them effectively


• Defining model validation strategies and operationalizing them


• Defining the preprocessing or feature engineering to be done on a given dataset


• Defining data augmentation pipelines


• Training models, tuning their hyperparameters, and optimizing output


• Analyzing the errors of the model and designing strategies to overcome them


• Deploying models to production


• Stay current with emerging technologies and industry trends

Qualifications, Skills and Experience

• PhD in mathematics, statistics, computer science, physics, economics or another quantitative field


• Proficiency with a deep learning framework such as TensorFlow, PyTorch, CNTK, etc.


• Excellent understanding of machine learning techniques and algorithms, such as GLM, k-NN, Naive Bayes, SVM, Random Forest, Gradient Boosting, etc.


• Excellent understanding of deep learning algorithms, such as CNN, RNN, etc.


• Experience using transfer learning and fine tuning on pre-trained models.


• Expertise in natural language processing, such as topic modelling, entity extraction, etc.


• Expertise in visualizing and manipulating big datasets and SQL.


• Proficiency with OpenCV


• Familiarity with Linux


• Ability to select hardware to run an ML model with the required latency


• Experience using statistical programming with at least one language such as Python, R, Scala


• Proficiency with machine learning libraries for Python (scikit-learn, Pandas, SciPy, NumPy, etc)


• Experience developing data visualizations using a tool such as Matplotlib, Shiny, Bokeh, etc.


• Experience in command line and scripting languages


• Experience working in parallel, distributed systems with technologies such as Spark / PySpark, Hadoop, Databricks, etc.


• Ability to effectively present and explain complex concepts to a non-technical audience


• Experience in developing deep learning data pipelines and model deployments


• Actuarial background will be of advantage