Arcolab - Data Scientist - Predictive Modeling (4-8 yrs)

Role Summary :

Data Scientist applies strong expertise in machine learning, data mining, and information retrieval to design, prototype, and build AI engine and services.

The data scientist collaborates with business partners to define the technical problem statement and hypothesis to test; develops efficient and accurate analytical models that mimic business decisions and incorporates them into analytical data products or tools with the support of a cross-functional team

Key Responsibilities :

1. Modelling and developing Machine Learning solutions

2. Carrying out pre-processing, cleansing of structured and unstructured data for ML models

3. Implement solutions for prediction systems and machine learning challenges - Monitor and Maintain ML/AI builds post implementation

4. Follow Quality Assurance (QA) & Compliance processes to ensure quality Automations

5. Updating and maintaining Standards around Industry best practices

Domain Expertise :

1.B- Tech/MSc/PhD (Computer Science/ Computer Applications)

2. Minimum 4 years of experience in a Data Scientist role

3. Experience in developing ML based solutions or functionality

4. Experience in Cloud technologies

5. Must be able to multitask and willing to work on multiple projects at a given time.

- Strong written and verbal communication skills

7. Ability to work to deadlines and manage expectations

8. Strong analytical and problem-solving skills

Individual Skills :

- Professional attitude and service orientation; team player

- Quick learner to business needs and translate them into potential analytics solutions

- Number sense; healthy skepticism around validity of data and its inherent biases

- Good verbal and written communication skill

Mindset & Behaviors:

- Exemplary organizational skills with attention to detail & accuracy

- Ability to coach, mentor, and collaborate with business partners, analysts, and team members

- Attitude to thrive in a fast-paced environment

- Open minded to new approaches of learning