Data Scientist

BatchService is a fast-growing SaaS company. We offer lead generation and data solutions software, helping more than 10,000 real estate investors, agents, and brokers from small and medium-sized businesses. At BatchService, we help businesses streamline productivity. We understand the challenges growing businesses face. That’s precisely why working at BatchService is so special. Every day, our software allows businesses to focus on what really matters: growing their businesses. BatchService is not your average working experience. It’s more than a job. We have the energy and boldness of a startup, with the expertise and pragmatism of a scale-up. All in one workplace.
**Job Overview**:
**Responsibilities and Duties**:

- Work with an agile full-stack scrum team to deploy Data Science and Machine Learning models into production
- Develop new DS and ML based services that enhance our data capabilities
- Design and implement scalable and repeatable DS and ML pipelines
- Work closely with business to identify issues and use data to propose solutions for effective decision making
- Formulating, suggesting, and managing data-driven projects which are geared at furthering the business's interests.
- Devise and utilize algorithms and models to mine big data stores, perform data and error analysis to improve models, and clean and validate data for uniformity and accuracy
- Data mining using state-of-the-art methods or extracting usable data from valuable data sources and undertake preprocessing of structured and unstructured data
- Processing, cleansing, and validating the integrity of data to be used for analysis
- Analyze large amounts of information to discover trends and patterns
- Build algorithms and design experiments to merge, manage, interrogate and extract data to supply tailored reports to colleagues, customers or the wider organization
- Experiment against data points, provide information based on experiment results and provide previously undiscovered solutions to command data challenges.
- Develop processes and tools to monitor and analyze model performance and data accuracy
- Build predictive models and machine learning algorithms
- Selecting features, building and optimizing classifiers using machine learning techniques
- Combine models through ensemble modeling
- Present information using data visualization techniques
- Propose solutions and strategies to business challenges
- Develop company A/B testing framework and test model quality.
- Collaborate with different functional teams to implement models and monitor outcomes.
- Creating automated anomaly detection systems and constant tracking of its performance
- Implement analytical models into production by collaborating with data engineers and software engineers
- Conduct research from which you'll develop prototypes and proof of concepts
- Look for opportunities to use insights/datasets/code/models across other functions in the organization
- Execute analytical experiments methodically to help solve various problems and make a true impact
- Doing ad-hoc analysis and presenting results in a clear manner
- Maintain clear and coherent communication, both verbal and written, to understand data needs and report results

**Qualifications and Skills**:

- Bachelors/Masters degree in Computer Science or Engineering discipline
- 8+ years of overall IT experience
- 5+ years of proven experience in a similar role
- 5+ years experience deploying DS and ML models in a production environment
- Solid hands-on experience with a broad range of DS and ML technologies.
- Solid experience in Databricks Lakehouse Platform for Data Science & Machine Learning: Databricks Machine Learning
- Experience with common data science toolkits, such as R, Weka, NumPy, MatLab, etc. depending on specific project requirements. Excellence in at least one of these is highly desirable
- Strong programming experience in statistical programming languages like R, Python, and database query languages like SQL, Hive, Pig is desirable. Familiarity with Scala, SAS, Java or C++ is an added advantage.
- Experience with NoSQL databases, such as MongoDB, Cassandra, HBase
- Solid expertise in Data Structures, Algorithms, Statistics, Apache Spark, AWS, Tableau, D3.js, etc.
- Proficiency in Statistics is essential: Good applied statistical techniques and concepts, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators etc.
- Strong Math Skills (Multivariable Calculus and Linear Algebra) - understanding the fundamentals of Multivariable Calculus and Linear Algebra is important for predictive performance or algorithm optimization techniques.
- Solid experience in machine learning methods like k-Nearest Neighbors, Naive Bayes, SVM, Decision Forests, etc.
- Experience in variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
- Knowledge and experience in statist