The race condition in the provided code arises because multiple threads can access and modify the shared dictionaries `self.jobs` and `self.results` simultaneously. Specifically, one thread might be reading a job from `self.jobs` while another thread deletes it or writes to `self.results`, leading to data corruption or `KeyError` exceptions.

To fix this, we need to use a `threading.Lock` to ensure that only one thread can execute the critical sections (accessing or modifying `self.jobs` and `self.results`) at a time.

Here is the complete fixed implementation:

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        # Initialize a lock to manage access to shared resources
        self.lock = threading.Lock()

    def add_job(self, job_id, data):
        # Lock to ensure adding a job is thread-safe
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        # Lock to ensure the entire transaction (check, process, store, delete) 
        # is atomic. This prevents multiple threads from processing the same 
        # job or writing results concurrently.
        with self.lock:
            if job_id not in self.jobs:
                return False

            data = self.jobs[job_id]
            
            # Execute the processor. 
            # Note: Holding the lock during processing blocks other threads 
            # from accessing the queue. For high-concurrency systems, 
            # it is often better to 'pop' the job from the queue before 
            # processing to release the lock, but this preserves the 
            # original logic where the job remains in 'jobs' until the 
            # result is stored (e.g., for retry logic on failure).
            result = processor(data)
            
            self.results[job_id] = result
            del self.jobs[job_id]
        return True

    def get_result(self, job_id):
        # Lock to ensure reading the result is consistent
        with self.lock:
            return self.results.get(job_id)
```

### Key Changes:
1.  **Added `self.lock = threading.Lock()`** in `__init__`.
2.  **Wrapped `add_job` logic** in `with self.lock:` to prevent race conditions when adding new jobs.
3.  **Wrapped the entire logic of `process_job`** in `with self.lock:`. This ensures that:
    *   The check `if job_id not in self.jobs` and the read `data = self.jobs[job_id]` are atomic.
    *   No other thread can delete the job or write to results while the current thread is processing it.
    *   The write to `self.results` and the deletion from `self.jobs` happen safely.
4.  **Wrapped `get_result` logic** in `with self.lock:` for consistency.

*Note: In a production environment with long-running tasks, holding the lock during `processor(data)` can block the queue. A more optimized approach would be to lock only to "claim" the job (pop it from `self.jobs`), process it outside the lock, and then lock again to store the result. However, the solution above is the most direct fix that preserves the original code's error-handling behavior (keeping the job in the dictionary if processing fails).*