TH - Machine Learning Consultant

Thinking Machines is a technology consultancy building AI & data platforms to solve high impact problems for our clients. Our vision is for Southeast Asia to become a global hub for data science. To do that, we create data cultures one organization at a time.

We’re a company made up of intellectually curious, civic-minded, forever-learning individuals. We believe that great data science products are built with care for people, and that the best way to drive inclusive innovation is to start with a diverse team.

Our field of work is incredibly dynamic, so we want to work with people who are committed to growing with us. We want to hire people who can demonstrate an ability to learn, then provide them with personalized coaching, growth opportunities, and a great working environment to get them to world-class.

This is an opportunity for someone with a strong portfolio of work in machine learning and analytics to do great work in the company of a high-performance team. This job demands creativity, critical thinking, and a focus on delivering excellent work. You’ll be expected to effectively handle projects from day one, and will be provided with effective guidance as you learn and implement machine learning methods for our clients and internal products.

We’re a technology consultancy with constantly evolving responsibilities, and the following is an incomplete but representative list of things you can expect to be doing:

• Scoping work on ML/analytics use cases: working with clients to understand their needs, and designing quantitative solutions to address those needs whether through machine learning, analytics, data pipelies and visualization or other methods.
• Exploring data and its sources: looking for patterns and signals in the data, identifying important fields, gleaning insights, and discerning whether machine learning can even apply to the problem
• Developing and evaluating machine learning models: feature engineering, experimenting with different model types, choosing the right metrics and measuring performance, analysis of model performance and results (e.g. feature importance, model stability, performance across different groups), etc.

• Understanding model design and decisions: explaining how models work under the hood and how a model arrived at a certain prediction, choosing the right model family, and justifying accuracy tradeoffs for faster performance

• Implementation strategy and application: recommend ways that the client can use the model outputs in their business, often involving collaboration with both the client and other Thinking Machines teammates (e.g., Strategists, Business Intelligence Analysts, to name a few).

• Research and keeping up with the tech space: reading through published ML papers, and implementing improved versions of their models in new domains

• Acquiring, cleaning, and evaluating the integrity of datasets! Let’s be real, this is a tricky and satisfying 25% of the job. We use Google Cloud Platform for many components of our machine learning workflow.

In addition, senior ML Consultant are also expected to assume technical leadership roles. On projects, they must be able to plan out roadmaps for ML/analytics solutions delivery, own a project's ML/analytics components, and serve as a point-person for client interactions regarding these components. They must be able to offer sound technical guidance to junior staffers and the rest of the project team members.

We’re looking for someone who meets the following profile:

• Comfort with machine learning models - You have a demonstrated understanding of classic machine learning models for classification and regression (e.g. decision trees, ensemble models, SVMs etc). Applicants who are aiming for more senior positions in this role must have advanced knowledge in at least one specific area. Here are some rough categories:
  • Sequential Prediction Models (e.g. time series forecasting, LSTMs etc)
  • Computer Vision (e.g. CNNs, SSDs etc.)
  • Language Models (e.g. word embeddings, text representations, etc)
• Comfort with code - You can use your local machine to scrape, load, and parse through moderately large datasets without much handholding.
• Clear communication - We help clients get the most out of their data, so we diagnose their needs effectively, do the analysis correctly, and communicate our findings in a way that leads to understanding and action. At minimum, you need to have the ability to articulate your points logically, and have to be willing to learn this skillset as you grow with us.
• Application-conscious approach to solutions - You should be able to design and develop solutions that will have a clear potential impact on clients' work and have a vision on how they might use it in real-world applications.
• Productive curiosity - You ask a lot of the right questions. Find a surprising correlation? Dig into the raw data to validate it.
• Enjoys both teaching and learning - We believe that data science is an incredible field to be in today. There’s a huge amount of new material to learn, we all want to learn it, and we’re looking for someone who wants to contribute to everyone’s growth. As part of our job interview, we’ll be asking you to read and summarize a machine learning paper for us.
• Strong sense of initiative—You’re always looking for ways to be useful and you hate having nothing to do.
• Social intelligence— It’s extremely important that you work well with others and thrive in an environment with lots of teamwork and interpersonal interaction.

• At least 2 years experience in data science, analytics or other related fields
  • If you have less than two years of experience in the field, be prepared to show a portfolio of Machine Learning Projects!
• Undergraduate/Graduate degrees in Computer Science, Physics, Mathematics, Statistics, or any related field
• Strong fundamental statistics skills, and linear algebra handling skills
• Strong quantitative analysis skills
• Familiarity with statistical programming languages like Python, or R
• Bonus Points (You’re not expected to have all the below qualifications but competitive candidates have at least two):
  • Knowing the scientific Python toolkit, and Deep Learning Frameworks (i.e. PyTorch, TensorFlow, Keras, etc.)
  • Publications in peer-reviewed journals and conferences
  • Comfort with cloud platforms (AWS or GCP) for training machine learning models on >10GB datasets
  • Domain expertise in a non-tech field. Are you an expert in energy, finance, insurance? Bring something to the table that we don’t yet have!
  • Fluency with the open source frameworks and good software engineering practices

We offer the following compensation and benefits:

• Competitive salary — the compensation amount is positively correlated with the difficulty of the job, relevant experience, fit, and skill factors.
• Fully remote — due to the global pandemic, we have shifted to a fully remote company for the foreseeable future while we monitor the situation.