JOB DESCRIPTION

This is a remote position.

HOW DO YOU KNOW IF YOU ARE A GOOD FIT?

• 5+ years of experience in developing and implementing machine learning models in a production environment.
• Strong understanding of machine learning, including deep learning, time series analysis, and anomalous pattern detection.
• Extensive experience with various machine learning techniques for classification, regression, clustering, and forecasting tasks (e.g., recurrent neural networks, ARIMA models, RCNNs).
• Proficiency in Python and relevant machine learning libraries (scikit-learn, TensorFlow, PyTorch).
• Experience with data manipulation and analysis libraries (e.g., Pandas, NumPy).
• Experience with data visualization tools (e.g., Matplotlib, Seaborn).
• Solid understanding of statistical concepts and techniques.
• Experience with feature engineering and model selection.
• Excellent communication and collaboration skills.
• Familiarity with cloud computing platforms (e.g., AWS, Azure, GCP).

Preferred Qualifications

• Experience working with the Google Cloud Platform (GCP) and its machine learning services (e.g., Vertex AI, AI Platform, BigQuery ML).
• Experience in developing and deploying models in a geospatial context is preferred.
• Experience with maritime data is a plus.

What do we offer?

• Projects/clients: Get the stability of a larger group and the excitement of a startup. Work on interesting global projects, challenges and technologies for top brands, fast-paced startups and governments. Experience working with a mature SCRUM team and day-to-day Agile mindset project implementation.
• Team and working environment: Join our diverse remote builders’ community and hubs oriented in Indonesia, Cambodia, Singapore, and Thailand. Experience an optimized remote working environment.
• At Slash, we’re proud of our collaborative culture; everyone is ready to lend a helping hand, whether it’s your first day on the job or your thousandth. We also embrace a trust-centric culture, flexibility and accountability first, and a work-life integration mindset.
• Learning: We focus on the growth of both parties. You will meet communities packed with intelligent peers and attend events with regular speakers, so you don’t miss out on the latest tech trends. Slash is a safe space for people to share if you’re passionate about something. Our favorite learning practices are the Growth Accountability Group, Mentoring Program, Venture Club, and Leadership. On top of your remuneration, you will get annual training or learning credits to be redeemed. We actively encourage everyone to suggest training beneficial for their professional growth.
• Technology: We work on a wide-range of technologies: latest web and mobile technologies, AI, blockchain, latest Cloud services, microservices & middlewares, security & infrastructure, etc.

How do you apply?
Interested applicants are encouraged to submit application through our career site or submit your CV to join@slash.co
For more information, visit us at www.Slash.co

ROLE AND RESPONSIBILITIES, AND HOW WILL THE ROLE IMPACT SLASH?

About the role
We are seeking a highly motivated and experienced Machine Learning/Data Science Contractor to join our team in developing cutting-edge solutions for a critical maritime-related project. As a key member of our development team, you will play a crucial role in designing, building, and implementing machine learning models to predict and analyze patterns from various data sources. You will be working extensively with spatiotemporal data, developing solutions that integrate geospatial and temporal patterns to deliver actionable insights. This is a challenging and impactful project with the potential to significantly improve risk management in a key sector. This role requires a strong background in machine learning and significant experience with the Google Cloud Platform (GCP). If you’re passionate about building cutting-edge solutions and working in a dynamic environment, we’d love to hear from you!

KEY RESPONSIBILITIES

• Model Development & Implementation: Develop, and implement machine learning models to predict and classify based on various data sources, including activity data, historical records, and environmental data.
• Data Exploration & Preprocessing: Perform exploratory data analysis (EDA) to understand the characteristics of data, identify patterns, and prepare data for model training.
• Feature Engineering: Create and select relevant features from raw data to improve model performance and interpretability.
• Model Selection & Evaluation: Experiment with different machine learning algorithms (supervised, semi-supervised, and unsupervised) and evaluate their performance using appropriate metrics.
• Algorithm Implementation: Develop and implement algorithms using modern tools, and coding practices, to ensure scalability and maintainability.
• Deployment: Containerize the algorithm for deployment on a cloud based system (GCP).
• Model Tuning & Optimization: Fine-tune model parameters to achieve optimal performance and address issues such as overfitting and bias.
• Collaboration: Collaborate with data engineers to process and analyze large datasets, leveraging GCP services such as BigQuery, Dataflow, and Dataproc.
• Staying Current: Stay up-to-date with the latest advancements in machine learning and deep learning techniques, particularly in the areas of sequence modeling, time series analysis, and anomaly detection.