Description

From building entire infrastructures or platforms to solving complex IT challenges, Cambridge Technology helps businesses accelerate their digital transformation and become AI-first businesses. With over 20 years of expertise as a technology services company, we enable our customers to stay ahead of the curve by helping them figure out the perfect approach, solutions, and ecosystem for their business.Our experts help customers leverage the right AI, big data, cloud solutions, and intelligent platformsthat will help them become and stay relevant in a rapidly changing world.

Role and Responsibilities

• The ideal candidate will have a bachelors degree in data science, statistics, or a related discipline with 4-6 years of experience, or a master’s degree with 4-6 years of experience. A strong candidate will also possess many of the following characteristics Strong problem-solving skills with an emphasis on achieving proof-of-concept Knowledge of statistical techniques and concepts (regression, statistical tests, etc.) Knowledge of machine learning and deep learning fundamentals Experience with Python implementations to build ML and deep learning algorithms (e.g., pandas, numpy, sci-kit-learn, stats models, Keras, PyTorch, etc.) Experience writing and debugging code in an IDE Experience using managed web services (e.g., AWS, GCP, etc.) Strong analytic and communications skills Curiosity, flexibility, creativity, and a strong tolerance for ambiguity Ability to learn new tools from documentation and internet resource. Roles and responsibilities : You will work on a small, core team alongside other engineers and business leaders throughout Cambridge with the following responsibilities: Collaborate with client-facing teams to design and build operational AI solutions for client engagements Identify relevant data sources for data wrangling and EDA Identify model architectures to use for client business needs Build full-stack data science solutions up to MVP that can be deployed into existing client business processes or scaled up based on clear documentation Present findings to teammates and key stakeholders in a clear and repeatable manner