Data Scientist (Remote)

UP.Labs; Santa Monica, California (CA, MO); Remote; USD 90,000 - 150,000; Full time

Job summary

An innovative firm is seeking a Mid-Senior Data Scientist to drive data science initiatives from concept to reality. In this pivotal role, you will collaborate with dynamic venture teams to validate business ideas and develop scalable MVPs, leveraging your expertise in machine learning and data engineering. This role offers the opportunity to work in a vibrant, inclusive environment where your contributions will directly impact the growth of technology startups focused on transforming the movement of people and goods. If you are passionate about data science and eager to make a difference, this is the perfect opportunity for you.

Qualifications

• 4-7 years in Data Science with hands-on experience in ML algorithms.
• Strong background in data cleansing, optimization, and product delivery.

Responsibilities

• Lead data science efforts and create proofs of concept for startups.
• Mentor team members and support data-driven decision-making.

Skills

Machine Learning

Data Science

Statistical Analysis

Data Engineering

Optimization Algorithms

Feature Engineering

Time Series Analysis

Agile Methodologies

Collaboration Skills

Education

MSc in Statistics

PhD in Data Science

Tools

Oracle

DataBricks

AWS

GCP

Azure

Job description

Job Description: Mid-Senior Data Scientist

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

You'll act as the hands-on expert and owner of data science for a technology venture. You will collaborate with our venture teams to drive day-to-day data science efforts. This includes validating initial business concepts, ideating and validating data use cases, developing proof of concepts, and working closely with product and engineering team members to drive practical outcomes.

In this role, you will:

1. Act as the owner of Data Science, Analytics, and in some cases Data Engineering as a subject matter expert.
2. Create rapid proofs of concept, then scale into functional MVPs to turn concepts into tangible reality.
3. Mentor and support team members to achieve greater outcomes at a larger scale in data density and system complexity.
4. Enjoy working in a diverse, dynamic, collaborative, transparent, and inclusive environment where all ideas and opinions are valued.

You should have:

1. MSc or PhD in a quantitative field (Statistics, Math, Economics, or Data Science preferred) with 4-7 years of experience in Data Science and Machine Learning domains and their practical applications.
2. Hands-on experience with classic optimization and machine learning algorithms: regressions, operations research, constraint optimization, integer programming, intervention analysis, xgboost, lgbm, random forest, and anomaly detection algorithms for building, evaluating, deploying, and monitoring ML models from scratch.
3. Experience with filtering and cleansing unstructured (or ambiguous) data into usable data sets that can be analyzed to extract insights and used for feature engineering.
4. Hands-on and end-to-end product build, development, and delivery experience.
5. Ability to clearly explain technical concepts in English.
6. Experience with Databases, Data Warehousing, and ETL systems and solutions, e.g., Oracle, DataBricks, and respective public cloud service offerings from AWS, GCP, and Azure.
7. Familiarity with time series analysis, LSTM (deep learning approaches for sequence analysis are a plus).
8. Familiarity and preference for working in ambiguous, fast-paced environments such as startups and growth-phase tech companies.
9. Experience working with or managing and leading remote, distributed teams including full-time data scientists, engineers, and vendors/contractors.
10. Awareness of the latest in Data Science and Data Engineering trends, as well as new use cases within the ML Space.
11. Experience working with agile, lean, and Continuous Delivery approaches, such as Continuous Integration, TDD, Infrastructure as Code, etc.
12. Experience in collaborating with Product teams to find effective solutions.
13. An open, curious, and humble mindset that contributes to our open, inclusive, and collaborative environment.

Additional desired competencies:

1. Experience with systems planning in the domains of transportation, aviation, or digital simulation would be valuable.
2. Experience working with major cloud environments (Azure, GCP, AWS) and cloud-native software architectures.
3. Knowledge of Bayesian models, Monte Carlo simulations, and survival methods is beneficial.
4. Familiarity with AB testing setup and analysis.
5. Familiarity with Reinforcement Learning for practical use cases.

UP.Labs Summary:

We build high-growth technology startups that enable faster, cleaner, and safer movement of people and goods. Our vision is to transform the moving world by pairing leading corporations and entrepreneurs with a proven methodology for launching and scaling software and hardware companies.