Data Scientist at Valsoft Corporation

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

18 Dec, 25

Salary

0.0

Posted On

19 Sep, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, R, SQL, Machine Learning, Data Engineering, Data Visualization, Azure, Deep Learning, Data Wrangling, Feature Engineering, Communication, Problem Solving, Collaboration, Analytical Skills

Industry

Financial Services

Description

Exeevo is a global leader in customer relationship management (CRM) solutions, purpose-built for the Life Sciences industry. Our AI-powered, cloud-based platform unifies sales, marketing, medical, and service functions, enabling pharmaceutical, biotech, and medical device organizations to connect with stakeholders in transformative and innovative ways. By leveraging advanced technologies, Exeevo empowers Life Sciences companies to redefine how they engage with healthcare professionals, patients, and other key audiences, driving growth and delivering measurable business impact. Serving thousands of users across pharmaceutical and medical device companies globally, Exeevo has a strong international presence with offices in China, India, Europe, and North America. About the Role: We are seeking a talented and driven Data Scientist to join our team in the life sciences industry. The ideal candidate will be responsible for a complete end-to-end data lifecycle, from ingesting raw data from Dataverse and external sources to building and deploying machine learning models in a production environment. The successful candidate will have a strong technical background, a deep understanding of data science fundamentals, and excellent communication skills. Responsibilities: Data Ingestion and Engineering: Acquire and process raw data from various sources, including Microsoft Dataverse and third-party applications. Design and implement robust data pipelines on the Azure platform to clean, transform, and prepare data for analysis and model training. Machine Learning Model Development & Deployment: Develop, train, and evaluate machine learning models for specific business problems, such as Next Best Action (NBA) and content recommendations. Utilize the Azure ML stack to deploy models into production, ensuring seamless and scalable consumption by Dynamics and custom applications. Collaboration & Communication: Work closely with clients to understand their data requirements and business needs. Use data visualization to communicate complex insights and model results to both technical and non-technical stakeholders. Collaborate with data engineers and product teams to ensure data quality, pipeline efficiency, and product alignment. Skills and Qualifications: Mandatory Skills & Experience: Technical Foundation: Programming Proficiency: Mastery of Python and/or R, with strong SQL skills for data manipulation. Hands-on experience with critical libraries such as pandas, NumPy, scikit-learn, and deep learning frameworks like TensorFlow or PyTorch. Machine Learning Algorithms: Deep understanding of supervised and unsupervised learning, ensemble methods, and deep learning architectures. Proven ability to apply appropriate techniques for regression, classification, clustering, and neural network models. Data Expertise: Data Wrangling & Engineering: Expertise in cleaning, transforming, and preparing messy, real-world data. This includes handling missing values, identifying outliers, and performing advanced feature engineering. Data Visualization & Storytelling: Ability to create compelling data visualizations using tools like Matplotlib, Plotly, or Tableau to effectively communicate insights and tell a data-driven story. Azure & Cloud Experience: Proven experience in designing and implementing data pipelines in an Azure environment. Soft Skills: Excellent verbal and written communication skills with the ability to articulate complex technical concepts to a diverse audience, including clients and internal teams. Strong problem-solving and analytical skills with a proactive approach to identifying and addressing business challenges. Demonstrated ability to work effectively in a collaborative, team-oriented environment.

Responsibilities

The Data Scientist will manage the complete end-to-end data lifecycle, including data ingestion and machine learning model development. They will collaborate with clients to understand their data needs and communicate insights effectively.