Data Science Lead

Overview:

As the Lead Data Scientist at UP.Labs, you'll get to play a key and unique role in building new startups from 0 to 1 and creating practical outcomes from emergent technologies and accessible data.

You'll act as the principal expert while leading data science for a technology venture. You will collaborate with your venture team to design and implement data pipelines, guide data science best practices, oversee data monitoring, and build AI/ML solutions. Your duties will include assisting in validating the initial business concepts, ideating and validating AI/ML use cases, developing prototypes and proof of concepts, and working closely with the product and engineering team members to drive practical outcomes. You’ll have the support you need to drive new innovation in impactful domains ripe for change and growth and be at the forefront of data science and technology advancements in the field.

In this role, you will:

• Act as the primary owner of Data Science, Analytics, and in some cases Data Engineering as a subject matter expert

• Build out a team and incubate expertise amongst several venture start-ups

• Create rapid proofs of concept, then scale into functional MVPs to turn concepts into tangible reality.

• Mentor and support early-stage venture teams to achieve bigger outcomes at a greater scale in data density and system complexity.

• Cross-pollinate learnings, best practices, and insights between multiple ventures to drive continuous improvement of Engineering and Data Science at UP.Labs.

• Enjoy working in a diverse, dynamic, collaborative, transparent, and inclusive environment where all ideas and opinions are equally valued

You should have:

• 5+ years experience within Data Science, Data Engineering, and Machine Learning domains and their practical applications

• Hands on experience with machine learning algorithms like random forest, linear and logistic regressions, gradient boosting, classification, GANs, and anomaly detection algorithms for building, evaluating, deploying and monitoring ML models from scratch.

• Familiar with time series analysis, LSTM (and other deep learning approaches for sequence analysis)

• Familiarity with LLMs, including performance tuning and chaining patterns

• Familiarity and preference for working in ambiguous, fast-paced environments such as startups and growth-phase tech companies.

• Hands-on and end-to-end product build, development, and delivery experience.

• Experience working with or managing and leading remote, distributed teams including full-time data scientists, engineers and vendors/contractors.

• Awareness of the latest in Data Science and Data Engineering trends, as well as new use cases within the ML Space.

• Experience working with agile, lean, and Continuous Delivery approaches, such as Continuous Integration, TDD, Infrastructure as Code, etc

• Experience working with major cloud environments (Azure, GCP, AWS) and cloud-native software architectures.

• Experience with Databases, Data Warehousing, and ETL systems and solutions, e.g. Data Bricks, Snowflake, Fivetran, DBT and respective public cloud data infrastructure service offerings from AWS, GCP, and Azure.

• Strong experience in collaborating with Product teams to find effective solutions.

• Strong communication skills put to use by explaining technical vision and deeply technical concepts to a variety of multidisciplinary team members.

• An open, curious, and humble mindset that builds on to our open, inclusive, and collaborative environment.

Additional desired competencies:

• Experience with systems planning in the domains of transportation, aviation, or digital simulation would be valuable.

• Knowledge of Bayesian models, Monte Carlo simulations as well as MLS, ITSA, and MAB optimizers are beneficial